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PR_kwDOIPDwls5cInQw
| 11,490 |
Fix Open in Colab link for ClearML docs
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CONTRIBUTOR
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Description: Fixed the Open in Colab link for ClearML docs
Issue: https://github.com/allegroai/clearml/issues/1125
Twitter handle: DziezycMaciej
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PR_kwDOIPDwls5cImq1
| 11,489 |
Support Fireworks batching (#6)
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CONTRIBUTOR
| null |
Description
* Add `_generate` and `_agenerate` to support Fireworks batching.
Issue - Not applicable
Dependencies - None
Tag maintainer - @baskaryan
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PR_kwDOIPDwls5cIlJc
| 11,488 |
Added a better error description if processor name is wrong.
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CONTRIBUTOR
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Replace this entire comment with:
- **Description:** added a better error description for this error
- **Issue:** #11407
@baskaryan
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PR_kwDOIPDwls5cIdHG
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Rename RunnableMap to RunnableParallel
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[] | null | 1 | 2023-10-06T16:48:56 | 2023-10-09T10:22:04 | 2023-10-09T10:22:03 |
COLLABORATOR
| null |
- keep alias for RunnableMap
- update docs to use RunnableParallel and RunnablePassthrough.assign
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5cIKMo
| 11,486 |
bump 310
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I_kwDOIPDwls5zEPaV
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Issue: load_llm_from_config doesn't working with Chat Models
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[] | null | 1 | 2023-10-06T15:52:46 | 2023-10-11T23:29:10 | 2023-10-11T23:29:10 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
The llm loader `load_llm_from_config` defined [here](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/loading.py#L12) only returns `BaseLLM`s. There is no equivalent chat_model loader in langchain to load Chat Model LLMs from a config that I'm aware of.
What do you think of either extending the `load_llm_from_config` such that it returns both `BaseLLM` and `BaseChatModel`
```
load_llm_from_config(config: dict) -> Union[BaseLLM, BaseChatModel]
```
or create a chat_model loader in `langchain.chat_models.loading.py`
```
load_chat_llm_from_config(config: dict) -> BaseChatModel
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5cH_7E
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Add SemaDB VST wrapper
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[] | null | 3 | 2023-10-06T15:41:19 | 2023-10-12T02:09:39 | 2023-10-12T02:09:38 |
CONTRIBUTOR
| null |
- **Description**: Adding vectorstore wrapper for [SemaDB](https://rapidapi.com/semafind-semadb/api/semadb).
- **Issue**: None
- **Dependencies**: None
- **Twitter handle**: semafind
Checks performed:
- [x] `make format`
- [x] `make lint`
- [x] `make test`
- [x] `make spell_check`
- [x] `make docs_build`
Documentation added:
- SemaDB vectorstore wrapper tutorial
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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2. an example notebook showing its use. It lives in `docs/extras` directory.
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PR_kwDOIPDwls5cHlhs
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Add Cohere retrieval augmented generation to retrievers
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[] | null | 3 | 2023-10-06T14:48:51 | 2023-10-17T18:51:05 | 2023-10-17T18:51:05 |
CONTRIBUTOR
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Add Cohere retrieval augmented generation to retrievers
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PR_kwDOIPDwls5cHi0k
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rm brave
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COLLABORATOR
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PR_kwDOIPDwls5cHZpC
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Sentiment analysis chain
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[] | null | 3 | 2023-10-06T14:23:46 | 2023-10-21T11:52:05 | null |
NONE
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls5zDb5h
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Sentimental analysis chain
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NONE
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### Feature request
The SentimentAnalysis chain is designed to analyze the sentiment of textual data using langchain. It uses a language model like OpenAI's GPT-3.5 & 4 to process text inputs and provide sentiment labels and scores. This chain can be used for various applications, including sentiment analysis of product reviews, social media comments, or customer feedback.
### Motivation
To contribute to open-source
### Your contribution
yes , i am ready with my PR
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I_kwDOIPDwls5zDUqo
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Unable to use Python 3.12 due to `aiohttp` and other dependencies not supporting 3.12 yet
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[] | null | 7 | 2023-10-06T14:05:06 | 2023-11-15T13:56:41 | null |
NONE
| null |
### System Info
pip 23.2.1 from /usr/local/lib/python3.12/site-packages/pip (python 3.12)
Python 3.12.0 (main, Oct 3 2023, 01:48:15) [GCC 12.2.0] on linux
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# The issue
When I run either of these:
`pip3 install 'langchain[all]'` or
`pip install langchain`
The command fails with the full stack trace below.
# How to reproduce easily with docker
`docker run --rm -it python:3 pip install langchain`
# Stack Trace
```
Building wheels for collected packages: aiohttp, frozenlist, multidict, yarl
Building wheel for aiohttp (pyproject.toml) ... error
error: subprocess-exited-with-error
× Building wheel for aiohttp (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [160 lines of output]
*********************
* Accelerated build *
*********************
running bdist_wheel
running build
running build_py
creating build
creating build/lib.linux-x86_64-cpython-312
creating build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_ws.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/payload_streamer.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/helpers.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_runner.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/pytest_plugin.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/log.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_server.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/http.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/http_writer.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/formdata.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_exceptions.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/locks.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/http_websocket.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/test_utils.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/client_ws.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_urldispatcher.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_request.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_middlewares.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/client_proto.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/streams.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/tcp_helpers.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/client.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/typedefs.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/resolver.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/cookiejar.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/payload.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_fileresponse.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/worker.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/multipart.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/base_protocol.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/http_parser.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_protocol.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_log.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_routedef.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/http_exceptions.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/connector.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_app.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/hdrs.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/web_response.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/__init__.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/client_reqrep.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/tracing.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/client_exceptions.py -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/abc.py -> build/lib.linux-x86_64-cpython-312/aiohttp
running egg_info
writing aiohttp.egg-info/PKG-INFO
writing dependency_links to aiohttp.egg-info/dependency_links.txt
writing requirements to aiohttp.egg-info/requires.txt
writing top-level names to aiohttp.egg-info/top_level.txt
reading manifest file 'aiohttp.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'aiohttp' anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*.so' found anywhere in distribution
warning: no previously-included files matching '*.lib' found anywhere in distribution
warning: no previously-included files matching '*.dll' found anywhere in distribution
warning: no previously-included files matching '*.a' found anywhere in distribution
warning: no previously-included files matching '*.obj' found anywhere in distribution
warning: no previously-included files found matching 'aiohttp/*.html'
no previously-included directories found matching 'docs/_build'
adding license file 'LICENSE.txt'
writing manifest file 'aiohttp.egg-info/SOURCES.txt'
copying aiohttp/_cparser.pxd -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/_find_header.pxd -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/_headers.pxi -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/_helpers.pyi -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/_helpers.pyx -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/_http_parser.pyx -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/_http_writer.pyx -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/_websocket.pyx -> build/lib.linux-x86_64-cpython-312/aiohttp
copying aiohttp/py.typed -> build/lib.linux-x86_64-cpython-312/aiohttp
creating build/lib.linux-x86_64-cpython-312/aiohttp/.hash
copying aiohttp/.hash/_cparser.pxd.hash -> build/lib.linux-x86_64-cpython-312/aiohttp/.hash
copying aiohttp/.hash/_find_header.pxd.hash -> build/lib.linux-x86_64-cpython-312/aiohttp/.hash
copying aiohttp/.hash/_helpers.pyi.hash -> build/lib.linux-x86_64-cpython-312/aiohttp/.hash
copying aiohttp/.hash/_helpers.pyx.hash -> build/lib.linux-x86_64-cpython-312/aiohttp/.hash
copying aiohttp/.hash/_http_parser.pyx.hash -> build/lib.linux-x86_64-cpython-312/aiohttp/.hash
copying aiohttp/.hash/_http_writer.pyx.hash -> build/lib.linux-x86_64-cpython-312/aiohttp/.hash
copying aiohttp/.hash/_websocket.pyx.hash -> build/lib.linux-x86_64-cpython-312/aiohttp/.hash
copying aiohttp/.hash/hdrs.py.hash -> build/lib.linux-x86_64-cpython-312/aiohttp/.hash
running build_ext
building 'aiohttp._websocket' extension
creating build/temp.linux-x86_64-cpython-312
creating build/temp.linux-x86_64-cpython-312/aiohttp
gcc -fno-strict-overflow -Wsign-compare -DNDEBUG -g -O3 -Wall -fPIC -I/usr/local/include/python3.12 -c aiohttp/_websocket.c -o build/temp.linux-x86_64-cpython-312/aiohttp/_websocket.o
aiohttp/_websocket.c: In function ‘__pyx_pf_7aiohttp_10_websocket__websocket_mask_cython’:
aiohttp/_websocket.c:1475:3: warning: ‘Py_OptimizeFlag’ is deprecated [-Wdeprecated-declarations]
1475 | if (unlikely(!Py_OptimizeFlag)) {
| ^~
In file included from /usr/local/include/python3.12/Python.h:48,
from aiohttp/_websocket.c:6:
/usr/local/include/python3.12/cpython/pydebug.h:13:37: note: declared here
13 | Py_DEPRECATED(3.12) PyAPI_DATA(int) Py_OptimizeFlag;
| ^~~~~~~~~~~~~~~
aiohttp/_websocket.c: In function ‘__Pyx_get_tp_dict_version’:
aiohttp/_websocket.c:2680:5: warning: ‘ma_version_tag’ is deprecated [-Wdeprecated-declarations]
2680 | return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
| ^~~~~~
In file included from /usr/local/include/python3.12/dictobject.h:90,
from /usr/local/include/python3.12/Python.h:61:
/usr/local/include/python3.12/cpython/dictobject.h:22:34: note: declared here
22 | Py_DEPRECATED(3.12) uint64_t ma_version_tag;
| ^~~~~~~~~~~~~~
aiohttp/_websocket.c: In function ‘__Pyx_get_object_dict_version’:
aiohttp/_websocket.c:2692:5: warning: ‘ma_version_tag’ is deprecated [-Wdeprecated-declarations]
2692 | return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
| ^~~~~~
/usr/local/include/python3.12/cpython/dictobject.h:22:34: note: declared here
22 | Py_DEPRECATED(3.12) uint64_t ma_version_tag;
| ^~~~~~~~~~~~~~
aiohttp/_websocket.c: In function ‘__Pyx_object_dict_version_matches’:
aiohttp/_websocket.c:2696:5: warning: ‘ma_version_tag’ is deprecated [-Wdeprecated-declarations]
2696 | if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
| ^~
/usr/local/include/python3.12/cpython/dictobject.h:22:34: note: declared here
22 | Py_DEPRECATED(3.12) uint64_t ma_version_tag;
| ^~~~~~~~~~~~~~
aiohttp/_websocket.c: In function ‘__Pyx_CLineForTraceback’:
aiohttp/_websocket.c:2741:9: warning: ‘ma_version_tag’ is deprecated [-Wdeprecated-declarations]
2741 | __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/usr/local/include/python3.12/cpython/dictobject.h:22:34: note: declared here
22 | Py_DEPRECATED(3.12) uint64_t ma_version_tag;
| ^~~~~~~~~~~~~~
aiohttp/_websocket.c:2741:9: warning: ‘ma_version_tag’ is deprecated [-Wdeprecated-declarations]
2741 | __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/usr/local/include/python3.12/cpython/dictobject.h:22:34: note: declared here
22 | Py_DEPRECATED(3.12) uint64_t ma_version_tag;
| ^~~~~~~~~~~~~~
aiohttp/_websocket.c: In function ‘__Pyx_PyInt_As_long’:
aiohttp/_websocket.c:3042:53: error: ‘PyLongObject’ {aka ‘struct _longobject’} has no member named ‘ob_digit’
3042 | const digit* digits = ((PyLongObject*)x)->ob_digit;
| ^~
aiohttp/_websocket.c:3097:53: error: ‘PyLongObject’ {aka ‘struct _longobject’} has no member named ‘ob_digit’
3097 | const digit* digits = ((PyLongObject*)x)->ob_digit;
| ^~
aiohttp/_websocket.c: In function ‘__Pyx_PyInt_As_int’:
aiohttp/_websocket.c:3238:53: error: ‘PyLongObject’ {aka ‘struct _longobject’} has no member named ‘ob_digit’
3238 | const digit* digits = ((PyLongObject*)x)->ob_digit;
| ^~
aiohttp/_websocket.c:3293:53: error: ‘PyLongObject’ {aka ‘struct _longobject’} has no member named ‘ob_digit’
3293 | const digit* digits = ((PyLongObject*)x)->ob_digit;
| ^~
aiohttp/_websocket.c: In function ‘__Pyx_PyIndex_AsSsize_t’:
aiohttp/_websocket.c:3744:45: error: ‘PyLongObject’ {aka ‘struct _longobject’} has no member named ‘ob_digit’
3744 | const digit* digits = ((PyLongObject*)b)->ob_digit;
| ^~
error: command '/usr/bin/gcc' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for aiohttp
Building wheel for frozenlist (pyproject.toml) ... done
Created wheel for frozenlist: filename=frozenlist-1.4.0-cp312-cp312-linux_x86_64.whl size=261459 sha256=a163dbc3bcddc5bf23bf228b0774c1c783465a0456dd3ad5da6b3fa065ec3e23
Stored in directory: /root/.cache/pip/wheels/f1/9c/94/9386cb0ea511a93226456388d41d35f1c24ba15a62ffd7b1ef
Building wheel for multidict (pyproject.toml) ... done
Created wheel for multidict: filename=multidict-6.0.4-cp312-cp312-linux_x86_64.whl size=114930 sha256=d6cde2e1fe8812d821a35581f31f1ff4797800ad474bf9ec1407fe5398ba2739
Stored in directory: /root/.cache/pip/wheels/f6/d8/ff/3c14a64b8f2ab1aa94ba2888f5a988be6ab446ec5c8d1a82da
Building wheel for yarl (pyproject.toml) ... done
Created wheel for yarl: filename=yarl-1.9.2-cp312-cp312-linux_x86_64.whl size=285235 sha256=fb299f423d87aae65e64d9a2b450b6540257c0f1a102ee01330a02f746ac791c
Stored in directory: /root/.cache/pip/wheels/84/e3/6a/7d0fa1abee8e4aa39922b5bd54689b4b5e4269b2821f482a32
Successfully built frozenlist multidict yarl
Failed to build aiohttp
ERROR: Could not build wheels for aiohttp, which is required to install pyproject.toml-based projects
```
### Expected behavior
Installation succeeds
|
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I_kwDOIPDwls5zDF1c
| 11,478 |
Fail using langchain extractor with AzureOpenAI
|
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[] | null | 8 | 2023-10-06T13:29:21 | 2023-12-04T08:03:10 | null |
NONE
| null |
### System Info
pyython: 3.11
langChain:0.0.309
SO: windows 10
pip list:
Package Version
---------------------------------------- ---------
aiofiles 23.2.1
aiohttp 3.8.5
aiosignal 1.3.1
anyio 3.7.1
async-timeout 4.0.3
asyncer 0.0.2
attrs 23.1.0
auth0-python 4.4.0
backoff 2.2.1
bcrypt 4.0.1
beautifulsoup4 4.12.2
bidict 0.22.1
certifi 2023.7.22
cffi 1.15.1
chardet 5.2.0
charset-normalizer 3.2.0
chroma-hnswlib 0.7.3
chromadb 0.4.10
click 8.1.7
colorama 0.4.6
coloredlogs 15.0.1
cryptography 41.0.3
dataclasses-json 0.5.14
Deprecated 1.2.14
django-environ 0.10.0
docx2txt 0.8
emoji 2.8.0
faiss-cpu 1.7.4
fastapi 0.97.0
fastapi-socketio 0.0.10
filelock 3.12.2
filetype 1.2.0
flatbuffers 23.5.26
frozenlist 1.4.0
fsspec 2023.6.0
google-search-results 2.4.2
googleapis-common-protos 1.60.0
gpt4all 1.0.9
greenlet 2.0.2
grpcio 1.57.0
h11 0.14.0
html2text 2020.1.16
httpcore 0.17.3
httptools 0.6.0
httpx 0.24.1
huggingface-hub 0.16.4
humanfriendly 10.0
idna 3.4
importlib-metadata 6.8.0
importlib-resources 6.0.1
Jinja2 3.1.2
joblib 1.3.2
jsonpatch 1.33
jsonpointer 2.4
langchain 0.0.309
langdetect 1.0.9
langsmith 0.0.43
lxml 4.9.3
markdownify 0.11.6
MarkupSafe 2.1.3
marshmallow 3.20.1
monotonic 1.6
mpmath 1.3.0
multidict 6.0.4
mypy-extensions 1.0.0
nest-asyncio 1.5.7
networkx 3.1
nltk 3.8.1
nodeenv 1.8.0
numexpr 2.8.5
numpy 1.25.2
onnxruntime 1.15.1
openai 0.28.1
openapi-schema-pydantic 1.2.4
opentelemetry-api 1.19.0
opentelemetry-exporter-otlp 1.19.0
opentelemetry-exporter-otlp-proto-common 1.19.0
opentelemetry-exporter-otlp-proto-grpc 1.19.0
opentelemetry-exporter-otlp-proto-http 1.19.0
opentelemetry-instrumentation 0.40b0
opentelemetry-proto 1.19.0
opentelemetry-sdk 1.19.0
opentelemetry-semantic-conventions 0.40b0
overrides 7.4.0
packaging 23.1
pandas 1.5.3
pdf2image 1.16.3
Pillow 10.0.0
pip 23.2.1
playwright 1.37.0
posthog 3.0.2
prisma 0.9.1
protobuf 4.24.1
pulsar-client 3.3.0
pycparser 2.21
pydantic 1.10.12
pyee 9.0.4
PyJWT 2.8.0
pypdf 3.15.5
PyPika 0.48.9
pyreadline3 3.4.1
python-dateutil 2.8.2
python-dotenv 1.0.0
python-engineio 4.5.1
python-graphql-client 0.4.3
python-iso639 2023.6.15
python-magic 0.4.27
python-socketio 5.8.0
pytz 2023.3
PyYAML 6.0.1
regex 2023.8.8
requests 2.31.0
safetensors 0.3.2
scikit-learn 1.3.0
scipy 1.11.2
sentence-transformers 2.2.2
sentencepiece 0.1.99
setuptools 68.0.0
six 1.16.0
sniffio 1.3.0
soupsieve 2.5
SQLAlchemy 1.4.49
starlette 0.27.0
sympy 1.12
syncer 2.0.3
tabulate 0.9.0
tenacity 8.2.3
threadpoolctl 3.2.0
tiktoken 0.5.1
tokenizers 0.13.3
tomli 2.0.1
tomlkit 0.12.1
torch 2.0.1
torchvision 0.15.2
tqdm 4.66.1
transformers 4.31.0
typing_extensions 4.7.1
typing-inspect 0.9.0
tzdata 2023.3
unstructured 0.10.18
uptrace 1.19.0
urllib3 2.0.4
uvicorn 0.22.0
watchfiles 0.19.0
websockets 11.0.3
wheel 0.38.4
wrapt 1.15.0
yarl 1.9.2
zipp 3.16.2
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.chat_models import AzureChatOpenAI
from langchain.chains import create_extraction_chain
# Accessing the OPENAI_API_KEY KEY
import environ
DIR_COMMON = "./common"
env = environ.Env()
environ.Env.read_env(env_file=DIR_COMMON+"/.env_iveco")
# Schema
schema = {
"properties": {
"name": {"type": "string"},
"height": {"type": "integer"},
"hair_color": {"type": "string"},
},
"required": ["name", "height"],
}
# Input
inp = """Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde."""
# # Run chain
llm = AzureChatOpenAI(deployment_name="gpt4-datalab",model_name="gpt-4")
chain = create_extraction_chain(schema, llm,verbose= True)
print(chain.run(inp))
```
When i run i get:
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: Extract and save the relevant entities mentionedin the following passage together with their properties.
Only extract the properties mentioned in the 'information_extraction' function.
If a property is not present and is not required in the function parameters, do not include it in the output.
Passage:
Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.
Traceback (most recent call last):
File "C:\Sviluppo\python\AI\Iveco\LangChain\extract_1.1.py", line 28, in <module>
print(chain.run(inp))
^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chains\base.py", line 501, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chains\base.py", line 306, in __call__
raise e
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chains\base.py", line 300, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chains\llm.py", line 93, in _call
response = self.generate([inputs], run_manager=run_manager)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chains\llm.py", line 103, in generate
return self.llm.generate_prompt(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chat_models\base.py", line 469, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chat_models\base.py", line 359, in generate
raise e
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chat_models\base.py", line 349, in generate
self._generate_with_cache(
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chat_models\base.py", line 501, in _generate_with_cache
return self._generate(
^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chat_models\openai.py", line 345, in _generate
response = self.completion_with_retry(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chat_models\openai.py", line 284, in completion_with_retry
return _completion_with_retry(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\tenacity\__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\tenacity\__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\tenacity\__init__.py", line 314, in iter
return fut.result()
^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\concurrent\futures\_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\concurrent\futures\_base.py", line 401, in __get_result
raise self._exception
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\tenacity\__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\langchain\chat_models\openai.py", line 282, in _completion_with_retry
return self.client.create(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\openai\api_resources\chat_completion.py", line 25, in create
return super().create(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 155, in create
response, _, api_key = requestor.request(
^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\openai\api_requestor.py", line 299, in request
resp, got_stream = self._interpret_response(result, stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\openai\api_requestor.py", line 710, in _interpret_response
self._interpret_response_line(
File "C:\Users\vw603\Anaconda3\envs\python11\Lib\site-packages\openai\api_requestor.py", line 775, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: Unrecognized request argument supplied: functions
```
### Expected behavior
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: Extract and save the relevant entities mentionedin the following passage together with their properties.
Only extract the properties mentioned in the 'information_extraction' function.
If a property is not present and is not required in the function parameters, do not include it in the output.
Passage:
Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.
> Finished chain.
[{'name': 'Alex', 'height': 5, 'hair_color'
```
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I_kwDOIPDwls5zCkev
| 11,477 |
'RunnableLambda' object has no attribute 'run'
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[] | null | 1 | 2023-10-06T12:19:43 | 2023-10-06T12:30:19 | null |
NONE
| null |
### System Info
langchain==0.0.294
### Who can help?
@hwchase17 , @agola11
### 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
Build a `SimpleSequentialChain` including in its chains a `RunnableLambda`
It throws: `AttributeError: 'RunnableLambda' object has no attribute 'run'`
### Expected behavior
A RunnableLambda to have an attribute `run`...
Or rename the concept of Runnable.
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PR_kwDOIPDwls5cGncL
| 11,476 |
Fix: Correct trailing commas in JSON before parsing
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NONE
| null |
This commit addresses the issue where JSON strings with trailing commas would cause parsing to fail. We have added a preprocessing step to correct these commas before attempting to parse the JSON string using the `json.loads()` function.
Changes:
- Added a `correct_trailing_commas` function that removes trailing commas from a given JSON string.
- Used this function to preprocess the JSON string inside the `parse` method of `PydanticOutputParser`.
Reason:
Some JSON producing systems or manual entries might inadvertently add trailing commas, which are not valid in standard JSON. This change aims to make our parser more robust and forgiving of such common mistakes, thereby reducing potential disruptions or failures due to invalid JSON formats.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,930,035,345 |
I_kwDOIPDwls5zCgCR
| 11,475 |
Parser Error with Langchain/agents/Conversational/Output_parser.py
|
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[] | null | 2 | 2023-10-06T12:08:15 | 2023-10-16T23:33:53 | null |
NONE
| null |
### System Info
langchain==0.0.279
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
With GPT-3.5 turbo 16k model,
Query - What is universal workflow type?
Thought: Do I need to use a tool? Yes
Action: chat_with_datasource
Action Input: What is universal workflow type?
Observation: The Universal Workflow type represents logic flows in a system. It includes two types: Flows and Subflows.
Thought: Do I need to use a tool? No
raise OutputParserException(f"Could not parse LLM output: `{text}`")
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `Do I need to use a tool? No`
Analysis -
Although when I checked i found, its using
Langchain/agents/Conversational/prompt.py
--------------------------------------------------
FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:
```
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
```
When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
```
Thought: Do I need to use a tool? No
{ai_prefix}: [Your response here]
```"""
-----------------------------------------------------
So, when it does not get any tool to use, it should generate some response in "ai_prefix}:"may be previous response of llm/observation at last step"
but, its unable to generate any response and resulted into parser error.
However, when I run the same code with GPT-4, below is the response I get, and its able to parse the output as well, since llm is able to generate
{ai_prefix}: [Your response here] as per the prompt instruction, but GPT-3.5 model is unable to generate it.
-----------------------------------------------------------
Thought: Do I need to use a tool? Yes
Action: chat_with_datasource
Action Input: What is universal workflow type?
Observation: The Universal Workflow type represents logic flows in a system. It includes two types: Flows and Subflows.
Thought: Do I need to use a tool? No
AI: The Universal Workflow is a system that represents logic flows. It consists of different types such as Flows and Subflows.
------------------------------------------------------------
### Expected behavior
With GPT 3.5 model as well, it should generate AI: response, when agent found that it does not need to use a tool, which would fix the parse error.
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I_kwDOIPDwls5zCYLa
| 11,474 |
Add condition in Prompt based on the retriever
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[] | null | 11 | 2023-10-06T11:45:34 | 2023-11-14T05:18:43 | null |
NONE
| null |
### Issue you'd like to raise.
Hello,
I'm trying to change dynamically a prompt, based on the value of the context in a ConversationalRetrievalChain
```
prompt_template = """You are an AI assistant.
Use the following pieces of context to answer the question, in a precise way, at the end.
Context: {context if context else "some default: I don't have information about it"}
Question: {question}
Helpful Answer :"""
QA_PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
llm = AzureChatOpenAI(
temperature=0,
deployment_name="gpt-35-turbo",
)
memory = ConversationBufferWindowMemory(
k=1,
output_key='answer',
memory_key='chat_history',
return_messages=True)
retriever = vector.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .70})
qa_chain = ConversationalRetrievalChain.from_llm(llm=llm,
memory=memory,
retriever=retriever,
condense_question_prompt=CONDENSE_QUESTION_PROMPT,
combine_docs_chain_kwargs={'prompt': QA_PROMPT},
return_source_documents=True,
verbose=True)
```
The idea is when the retriever gets 0 documents, to have something in the context that push the model to say I don't know and don't make up and answer. I already try by adding in the prompt "if you don't know the answer don't make it up" and didn't work.
Any idea how to add a condition in the template or control the LLM based on the output of the retriever?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5cGVEH
| 11,473 |
Enhance the ImportError in bageldb.py
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[] | null | 1 | 2023-10-06T11:23:07 | 2023-10-06T11:23:32 | 2023-10-06T11:23:29 |
CONTRIBUTOR
| null |
Enhance the ImportError handling in the code to provide a more informative error message, guiding users on how to install the missing 'betabageldb' package using 'pip'. This change improves user experience and aids in debugging.
@eyurtsev
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Fix ClearML callback
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[] | null | 2 | 2023-10-06T10:44:29 | 2023-10-12T00:09:07 | 2023-10-12T00:09:02 |
CONTRIBUTOR
| null |
Handle different field names in dicts/dataframes, fixing the ClearML callback.
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I_kwDOIPDwls5zB758
| 11,471 |
Qdrant - Load the saved vector db/store ?
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[] | null | 6 | 2023-10-06T10:37:36 | 2024-01-08T12:29:50 | 2024-01-08T12:29:50 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I use Qdrant through langchain to store vectors. But I can't find any example in docs where the dataset is searched based on a previously created collection. How to load the data? I found that Pinecone has "from_existing_index" function, which probably does the thing. But Qdrant doesn't have such a function. I created my own solution using qdrant_client, but I would like to use Langchain to simplify the script. How to do it?
### Suggestion:
_No response_
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I_kwDOIPDwls5zB4xK
| 11,470 |
Vectorstore and ConversationalRetrievalChain should return the certainty of the relevance
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[] | null | 0 | 2023-10-06T10:29:22 | 2023-10-06T12:40:46 | null |
NONE
| null |
### Feature request
I am using Weaviate with ConversationalRetrievalChain
I am using `similarity_score_threshold` search type.
It will call `similarity_search_with_score` in `vectorstore/weaviate.py`
Currently it's written like this
` if not self._by_text:
vector = {"vector": embedded_query}
result = (
query_obj.with_near_vector(vector)
.with_limit(k)
.with_additional("vector")
.do()
)
else:
result = (
query_obj.with_near_text(content)
.with_limit(k)
.with_additional("vector")
.do()
)
`
The `with_additional` function can accept a list of `AdditionalProperties`
So there's no point of hardcoding to be just the vector, and let the user add the additional arguments required.
I've tested this just by changing ` .with_additional("vector")` to ` .with_additional(["vector", "certainty"] )` and it works just fine
### Motivation
I am using the Conversational Chain just because it's easier to manage than creating one. But it's limitations are apparent. I want to retrieve the scores it get which is currently not possible
### Your contribution
I don't know how to pass in the `_additional` arguments from the vectorstore.as_retriever() but just hard coding the args i want the tests worked fine.
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PR_kwDOIPDwls5cFyhl
| 11,469 |
Use InferenceClient instead of InferenceAPI for HuggingFaceHubEmbeddings
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NONE
| null |
**Description:**
Replace deprecated `InferenceAPI` of Hugging Face Inference with `InferenceClient` in the module `embeddings.huggingface_hub`.
**Issue:**
#10483
**Remarks:**
- A similar PR is open for the module `llms.huggingface_hub`: #10514
- It's my first contribution to an open source project. Then don't hesitate to send me your comments 😃.
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I_kwDOIPDwls5zBgRA
| 11,468 |
DOC: pydantic extraction example code not working
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| null |
### Issue with current documentation:
The first code example following the Pydantic section, right after "Lets define a class with attributes annotated with types."
results for me in the following pydantic errors using gpt-4:
RuntimeError: no validator found for <class '__main__.Properties'>, see `arbitrary_types_allowed` in Config
The following versions of langchain and pydantic were used:
langchain==0.0.309
pydantic==2.4.2
pydantic_core==2.10.1
### Idea or request for content:
It would be nice to have the code updated so that it works out of the box.
Perhaps a reference to the extraction functions in the 'openai functions' section would be nice.
|
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| 1,929,588,339 |
I_kwDOIPDwls5zAy5z
| 11,466 |
DOCX loader is not working properly in js
|
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[] | null | 2 | 2023-10-06T07:22:12 | 2023-10-09T05:59:57 | null |
NONE
| null |
### System Info
langchain latest version: 0.0.161
"mammoth": "^1.6.0",
### 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
Creating blob data with the use of fetch
const blobData = await fetch(totalFiles[i].url).then((res) => res.blob());
After this use this blob data in
const loader = new DocxLoader(blobData);
But When I hit the next step
const data = await loader.load();
It give me an error:
Error: Could not find file in options
at Object.openZip (unzip.js:10:1)
at extractRawText (index.js:82:1)
at DocxLoader.parse (docx.js:25:1)
at async langChainInitialization (index.js:88:20)
at async handleRefDocUpload (index.js:136:9)
### Expected behavior
It should load the document.
|
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| 1,929,475,722 |
PR_kwDOIPDwls5cEs40
| 11,465 |
Adds support for llama2 and fixes MPT-7b url
|
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[] | null | 3 | 2023-10-06T05:46:02 | 2023-10-11T03:34:36 | 2023-10-11T03:34:35 |
CONTRIBUTOR
| null |
- **Description:** This is an update to OctoAI LLM provider that adds support for llama2 endpoints hosted on OctoAI and updates MPT-7b url with the current one.
@baskaryan
Thanks!
|
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| 1,929,358,704 |
I_kwDOIPDwls5y_61w
| 11,464 |
The MIME type is 'video/mp4'. This file type is not currently supported in unstructured.
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[] | null | 1 | 2023-10-06T03:04:26 | 2023-10-06T03:09:36 | null |
NONE
| null |
### System Info
Langchain version : 0.0.292
Python version: 3.9.13
Platform: Apple M1, Sonoma 14.0
### Who can help?
@eyurtsev @hwc
### Information
- [x] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Have a google drive folder with multiple documents with formats like`.mp4`, `.md`, `google docs`, `google sheets` etc.
2. Try using `from langchain.document_loaders import GoogleDriveLoader`, it doesn't work as it only supports google docs, google sheets and pdf files.
```
folder_id = "0ABqitCqK0S_MUk9PVA" # have your own folder id, this is an example
loader = GoogleDriveLoader(
gdrive_api_file=GOOGLE_ACCOUNT_FILE,
folder_id= folder_id,
recursive=True,
# num_results=2, # Maximum number of file to load
)
```
3. Try using `from langchain.document_loaders import UnstructuredFileIOLoader` to load
```
folder_id = "0ABqitCqK0S_MUk9PVA" # have your own folder id, this is an example
loader = GoogleDriveLoader(
service_account_key=GOOGLE_ACCOUNT_FILE,
folder_id=folder_id,
file_loader_cls=UnstructuredFileIOLoader,
file_loader_kwargs={"mode": "elements"},
recursive=True
)
```
4. It throws the following error because `.mp4` files are not supported
`The MIME type is 'video/mp4'. This file type is not currently supported in unstructured.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [34], in <cell line: 3>()
1 start_time = time.time()
----> 3 docs = loader.load()
5 print("Total docs loaded", len(docs))
7 end_time = time.time() - start_time
File /opt/homebrew/Caskroom/miniforge/base/envs/py39/lib/python3.9/site-packages/langchain/document_loaders/googledrive.py:351, in GoogleDriveLoader.load(self)
349 """Load documents."""
350 if self.folder_id:
--> 351 return self._load_documents_from_folder(
352 self.folder_id, file_types=self.file_types
353 )
354 elif self.document_ids:
355 return self._load_documents_from_ids()
File /opt/homebrew/Caskroom/miniforge/base/envs/py39/lib/python3.9/site-packages/langchain/document_loaders/googledrive.py:257, in GoogleDriveLoader._load_documents_from_folder(self, folder_id, file_types)
252 returns.extend(self._load_sheet_from_id(file["id"])) # type: ignore
253 elif (
254 file["mimeType"] == "application/pdf"
255 or self.file_loader_cls is not None
256 ):
--> 257 returns.extend(self._load_file_from_id(file["id"])) # type: ignore
258 else:
259 pass
File /opt/homebrew/Caskroom/miniforge/base/envs/py39/lib/python3.9/site-packages/langchain/document_loaders/googledrive.py:316, in GoogleDriveLoader._load_file_from_id(self, id)
314 fh.seek(0)
315 loader = self.file_loader_cls(file=fh, **self.file_loader_kwargs)
--> 316 docs = loader.load()
317 for doc in docs:
318 doc.metadata["source"] = f"https://drive.google.com/file/d/{id}/view"
File /opt/homebrew/Caskroom/miniforge/base/envs/py39/lib/python3.9/site-packages/langchain/document_loaders/unstructured.py:86, in UnstructuredBaseLoader.load(self)
84 def load(self) -> List[Document]:
85 """Load file."""
---> 86 elements = self._get_elements()
87 self._post_process_elements(elements)
88 if self.mode == "elements":
File /opt/homebrew/Caskroom/miniforge/base/envs/py39/lib/python3.9/site-packages/langchain/document_loaders/unstructured.py:319, in UnstructuredFileIOLoader._get_elements(self)
316 def _get_elements(self) -> List:
317 from unstructured.partition.auto import partition
--> 319 return partition(file=self.file, **self.unstructured_kwargs)
File /opt/homebrew/Caskroom/miniforge/base/envs/py39/lib/python3.9/site-packages/unstructured/partition/auto.py:183, in partition(filename, content_type, file, file_filename, url, include_page_breaks, strategy, encoding, paragraph_grouper, headers, ssl_verify, ocr_languages, pdf_infer_table_structure)
181 else:
182 msg = "Invalid file" if not filename else f"Invalid file {filename}"
--> 183 raise ValueError(f"{msg}. The {filetype} file type is not supported in partition.")
185 for element in elements:
186 element.metadata.url = url
ValueError: Invalid file. The FileType.UNK file type is not supported in partition.
`
### Expected behavior
I would expect this error to be handled by the loader, simply ignore the unsupported files and load the rest. Can throw some warning to notify but not break the code.
|
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PR_kwDOIPDwls5cENSA
| 11,463 |
Quote tool names
|
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[] | null | 2 | 2023-10-06T02:19:44 | 2023-10-12T17:23:28 | null |
CONTRIBUTOR
| null |
- **Description:** Quote tool names in prompt templates
- **Issue:** https://github.com/langchain-ai/langchain/issues/1657 https://github.com/langchain-ai/langchain/issues/1477 https://github.com/langchain-ai/langchain/issues/1358
- **Tag maintainer:** @hwchase17
The original prompt template is ambiguous because it does not tell whether the tool names should be quoted or not. This PR makes it clear that the tool names should be quoted. It fixes #1358 for Nous-Hermes-Llama2-13b according to my tests.
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Vowpal wabbit/master
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| 1,929,286,434 |
PR_kwDOIPDwls5cEEpO
| 11,461 |
Use raise from statement so that users can find detailed error message
|
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[] | null | 1 | 2023-10-06T01:13:28 | 2023-10-13T15:38:06 | 2023-10-11T00:25:23 |
CONTRIBUTOR
| null |
- **Description:** Use `raise from` statement so that users can find detailed error message
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17
|
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| 1,929,282,468 |
I_kwDOIPDwls5y_oOk
| 11,460 |
Issue: how to use multiple inputs with custom llm agent
|
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[] | null | 1 | 2023-10-06T01:08:12 | 2023-10-06T01:14:37 | null |
NONE
| null |
### Issue you'd like to raise.
we are following https://python.langchain.com/docs/modules/agents/how_to/custom_llm_agent however, by default it's using one input only, how to make it accept multiple inputs same as STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, thanks.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5y_apY
| 11,459 |
OnlinePDFLoader crashes with import error
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[] | null | 3 | 2023-10-05T23:46:16 | 2023-10-06T02:00:33 | 2023-10-06T02:00:33 |
NONE
| null |
### System Info
langchain = "^0.0.304"
Python 3.11.5
MacBook Pro, Apple M2 chip, 8GB memory
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.document_loaders import OnlinePDFLoader
OnlinePDFLoader("https://www.africau.edu/images/default/sample.pdf").load()
```
I saw the following runtime import error:
```
ImportError: cannot import name 'PDFResourceManager' from 'pdfminer.converter' (/Users/{user}/Library/Caches/pypoetry/virtualenvs/replai-19tDtF2f-py3.11/lib/python3.11/site-packages/pdfminer/converter.py)
```
### Expected behavior
I expected `OnlinePDFLoader` to load the file.
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PR_kwDOIPDwls5cD1Np
| 11,458 |
Add LangSmith Run Chat Loader
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[] | null | 1 | 2023-10-05T23:27:54 | 2023-10-07T00:11:35 | 2023-10-07T00:02:19 |
COLLABORATOR
| null |
In case you don't want to make a dataset / easy to see the OAI formatted message equivalents
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PR_kwDOIPDwls5cDtml
| 11,456 |
docstrings `callbacks`
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[] | null | 2 | 2023-10-05T22:46:12 | 2023-10-06T00:13:14 | 2023-10-06T00:13:14 |
COLLABORATOR
| null |
Added missed docstrings to the `callbacks/`
|
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PR_kwDOIPDwls5cDq9m
| 11,455 |
WIP: move callbacks
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PR_kwDOIPDwls5cDp-N
| 11,454 |
"source" argument in constructor of Vectara
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[] | null | 1 | 2023-10-05T22:25:10 | 2023-10-06T02:23:52 | 2023-10-06T00:04:14 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** minor update to constructor to allow for specification of "source"
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ofermend
|
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PR_kwDOIPDwls5cDnpB
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Fix a Typo in Documentation
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[] | null | 2 | 2023-10-05T22:15:33 | 2023-10-05T22:34:06 | 2023-10-05T22:34:06 |
CONTRIBUTOR
| null |
- **Description:** This commit corrects a minor typo in the documentation. It changes "frum" to "from" in the sentence: "The results from search are passed back to the LLM for synthesis into an answer" in the file `docs/extras/use_cases/more/agents/agents.ipynb`. This typo fix enhances the clarity and accuracy of the documentation.
- **Tag maintainer:** @baskaryan
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Improve CLI ux
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[] | null | 1 | 2023-10-05T21:44:57 | 2023-10-05T23:40:01 | 2023-10-05T23:40:01 |
COLLABORATOR
| null |
Improve UX for cli
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adding support to modify documents in azure search vectorstore
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[] | null | 3 | 2023-10-05T20:47:46 | 2023-10-16T12:29:05 | null |
NONE
| null |
**Description**
Adding support to replace documents in Azure Cognitive Search. Right now, we can only upload documents but azure [azure-search-documents](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/azure/search/documents/_search_client.py#L623) provides the option to modify the documents.
**References**
- https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/azure/search/documents/_search_client.py#L623
- https://learn.microsoft.com/en-us/rest/api/searchservice/addupdate-or-delete-documents
**Issue**
None
**Dependencies**
None
**Tag maintainer**
@eyurtsev
**Example**
[azure_search_modify_documents.ipynb](https://github.com/bhanu-pappala/langchain/blob/azure_modify_documents/docs/extras/modules/vectorstore/azure_search_modify_documents.ipynb)
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PR_kwDOIPDwls5cDDqO
| 11,450 |
Skip `OpenAIWhisperParser` extremely small audio chunks to avoid api error
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[] | null | 2 | 2023-10-05T20:19:21 | 2023-10-09T13:52:37 | null |
NONE
| null |
**Description**
This PR addresses a rare issue in `OpenAIWhisperParser` that causes it to crash when processing an audio file with a duration very close to the class's chunk size threshold of 20 minutes.
**Issue**
#11449
**Dependencies**
None
**Tag maintainer**
@agola11 @eyurtsev
**Twitter handle**
leonardodiegues
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I_kwDOIPDwls5y-W47
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`OpenAIWhisperParser` raises error if audio has duration too close to the chunk limit
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[] | null | 0 | 2023-10-05T19:44:40 | 2023-10-05T19:56:36 | null |
NONE
| null |
### System Info
LangChain: 0.0.250
Python: 3.11.4
OS: Pop 22.04
### Who can help?
@agola11
@eyurtsev
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**Input**
```py
from pathlib import Path
from langchain.document_loaders.blob_loaders.schema import Blob
from langchain.document_loaders.parsers.audio import OpenAIWhisperParser
OPENAI_API_KEY = "<MY-KEY>"
audio_file = Path("~/Downloads/audio.mp3").expanduser()
audio_total_seconds = AudioSegment.from_file(audio_file).duration_seconds
print("Audio length (seconds):", audio_total_seconds)
blob = Blob.from_path(audio_file, mime_type="audio/mp3")
parser = OpenAIWhisperParser(api_key=OPENAI_API_KEY)
parser.parse(blob)
```
**Output**
```
Audio length (seconds): 1200.064
Transcribing part 1!
Transcribing part 2!
Attempt 1 failed. Exception: Audio file is too short. Minimum audio length is 0.1 seconds.
Attempt 2 failed. Exception: Invalid file format. Supported formats: ['flac', 'm4a', 'mp3', 'mp4', 'mpeg', 'mpga', 'oga', 'ogg', 'wav', 'webm']
Attempt 3 failed. Exception: Invalid file format. Supported formats: ['flac', 'm4a', 'mp3', 'mp4', 'mpeg', 'mpga', 'oga', 'ogg', 'wav', 'webm']
Failed to transcribe after 3 attempts.
```
### Expected behavior
Due to its internal rule of processing audio in [20-minute chunks](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/document_loaders/parsers/audio.py#L47), `OpenAIWhisperParser` is prone to crashing when transcribing audio with durations that are dangerously close to this limit. Given that OpenAI already has a very low audio length threshold of 0.1 seconds, a simple bypass could effectively resolve this issue.
```python
# https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/document_loaders/parsers/audio.py#L47
chunk_duration = 20
chunk_duration_ms = chunk_duration * 60 * 1000
chunk_length_threshold = 0.1
# Split the audio into chunk_duration_ms chunks
for split_number, i in enumerate(range(0, len(audio), chunk_duration_ms)):
# Audio chunk
chunk = audio[i : i + chunk_duration_ms]
if chunk.duration_seconds < chunk_length_threshold:
continue
```
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| 1,928,839,177 |
I_kwDOIPDwls5y98AJ
| 11,448 |
Error while importing LLMChain
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[] | null | 2 | 2023-10-05T18:32:29 | 2023-10-05T21:48:35 | null |
NONE
| null |
### Issue you'd like to raise.
When I'm running the code "from langchain.chains import LLMChain", I'm getting the following error:
'RuntimeError: no validator found for <class 're.Pattern'>, see `arbitrary_types_allowed` in Config'
<img width="887" alt="Screenshot 2023-10-06 at 12 01 49 AM" src="https://github.com/langchain-ai/langchain/assets/85587494/0b171391-bbc6-41f8-b237-17bc99b11b95">
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_No response_
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I_kwDOIPDwls5y9grN
| 11,447 |
Issue: Trying to be able to parse all parse of LLM outputs
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[] | null | 2 | 2023-10-05T17:10:23 | 2023-10-05T18:13:46 | null |
NONE
| null |
### Issue you'd like to raise.
I'm trying to make a general chat bot when ever I try to get it to write code I get [ERROR] OutputParserException: Could not parse LLM output:
is there a way to parse all parse of output?
Any help would be great.
### Suggestion:
_No response_
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PR_kwDOIPDwls5cCKAA
| 11,446 |
Remove str() from RunnableConfigurableAlternatives
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5cCGkv
| 11,445 |
fix api docs build
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PR_kwDOIPDwls5cCE14
| 11,444 |
Update langchain_release.yml
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Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5cB7u1
| 11,443 |
bump 309
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PR_kwDOIPDwls5cB3N8
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fixed return documents
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[] | null | 2 | 2023-10-05T16:09:46 | 2023-10-09T07:48:06 | 2023-10-05T16:37:09 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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| 1,928,619,715 |
I_kwDOIPDwls5y9GbD
| 11,441 |
Chroma is adding an extra space (very consistently) in front of '≤ ’ characters and it has a real impact.
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[] | null | 6 | 2023-10-05T16:00:27 | 2023-10-05T22:00:19 | null |
NONE
| null |
### System Info
**Specs:**
langchain 0.0.301
Python 3.11.4
embeddings =HuggingFace embeddings
llm = Claud 2
EXAMPLE:
1. Chunks object below in my code contains the following string: leflunomide **(LEF) (≤ 20 mg/day)**
`Chroma.from_documents(documents=chunks, embedding=embeddings, collection_name=collection_name, persist_directory=persist_db) `
2. after saving and retrieving from my local file with:
'db = Chroma(persist_directory=persist_db, embedding_function=embeddings, collection_name=collection_name)'
. . . then extracting with . . .
'db.get(include=['documents'])'
3. That string is now: leflunomide **(LEF) ( ≤20 mg/day)**, with a single space newly inserted before the ≤
This matters because it messes up retrieval augmentation with queries like “what doses of leflunomide are appropriate?” using Claude-2 as the llm
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Chunks object below in my code contains the following string: leflunomide **(LEF) (≤ 20 mg/day)**
`Chroma.from_documents(documents=chunks, embedding=embeddings, collection_name=collection_name, persist_directory=persist_db) `
2. after saving and retrieving from my local file with:
'db = Chroma(persist_directory=persist_db, embedding_function=embeddings, collection_name=collection_name)'
. . . then extracting with . . .
'db.get(include=['documents'])'
3. That string is now: leflunomide **(LEF) ( ≤20 mg/day)**, with a single space newly inserted before the ≤
This matters because it messes up retrieval augmentation with queries like “what doses of leflunomide are appropriate?” using Claude-2 as the llm
### Expected behavior
See description above
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I_kwDOIPDwls5y9D10
| 11,440 |
Issue: Is it possible to use a HF LLM using Conversational Retrieval Agent?
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[] | null | 2 | 2023-10-05T15:54:16 | 2023-10-10T11:04:40 | null |
NONE
| null |
### Issue you'd like to raise.
_No response_
### Suggestion:
_No response_
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I_kwDOIPDwls5y8vdQ
| 11,438 |
Bearer token support for self-deployed OpenLLM
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[] | null | 3 | 2023-10-05T15:09:13 | 2023-10-07T01:07:55 | null |
NONE
| null |
Hello there,
I have deployed an OpenLLM on a managed service that is protected by the use of an auth Bearer token:
```bash
curl -X 'POST' \
'https://themodel.url/v1/generate' \
-H "Authorization: Bearer Sh6Kh4[ . . . super long bearer token ]W18UiWuzsz+0r+U"
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"prompt": "What is the difference between a pigeon",
"llm_config": {
"use[ . . . and so on]
}
}'
```
Curl works like a charm.
In LangChain, I try to create my new llm as such:
```python
llm = OpenLLM(server_url="https://themodel.url/v1/generate", temperature=0.2)
```
But I don't know how to include this Bearer token, I even suspect it is impossible...
**Bot seems to confirm this is impossible as of 5 Oct 2023, as seen in https://github.com/langchain-ai/langchain/discussions/11437** ; this issue would then be a feature request.
Thanks!
Cheers,
Colin
_Originally posted by @ColinLeverger in https://github.com/langchain-ai/langchain/discussions/11437_
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| 1,928,443,676 |
PR_kwDOIPDwls5cBNOt
| 11,436 |
Fix .dict() for agent/chain
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[] | null | 1 | 2023-10-05T14:29:58 | 2023-10-05T14:51:22 | 2023-10-05T14:51:21 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5cAter
| 11,435 |
Add yandex stt parser
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CONTRIBUTOR
| null |
Description: Introducing an ability to load a transcription document of audio file using [Yandex SpeechKit](https://cloud.yandex.com/en-ru/services/speechkit)
Issue: None
Dependencies: yandex-speechkit
Tag maintainer: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5cAjwX
| 11,434 |
Fix PyMuPDFLoader kwargs
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[] | null | 2 | 2023-10-05T13:08:31 | 2023-10-05T20:25:20 | 2023-10-05T20:25:20 |
CONTRIBUTOR
| null |
- **Description:** Fix the `PyMuPDFLoader` to accept `loader_kwargs` from the document loader's `loader_kwargs` option. This provides more flexibility in formatting the output from documents.
- **Issue:** The `loader_kwargs` is not passed into the `load` method from the document loader, which limits configuration options.
- **Dependencies:** None
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I_kwDOIPDwls5y7lxm
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PGVector bound to a single database connection
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[] | null | 6 | 2023-10-05T12:52:39 | 2024-01-03T00:36:51 | null |
CONTRIBUTOR
| null |
### System Info
python: 3.11
langchain: 0.0.306
### Who can help?
Although PGVector implementation utilizes SQLAlchemy that supports connection pooling features, it gets one connection from the pool, saves it as a class attribute and reuses in all requests.
```python
class PGVector(VectorStore):
# ...
def __init__(
self,
# ...
) -> None:
# ...
self.__post_init__()
def __post_init__(
self,
) -> None:
self._conn = self.connect()
# ...
def create_vector_extension(self) -> None:
try:
with Session(self._conn) as session:
# ...
except Exception as e:
self.logger.exception(e)
def create_tables_if_not_exists(self) -> None:
with self._conn.begin():
Base.metadata.create_all(self._conn)
@contextlib.contextmanager
def _make_session(self) -> Generator[Session, None, None]:
yield Session(self._conn)
# ...
```
This means that:
- Since the same connection is user for handle all requests, they will not be executed in parallel but rather enqueued. In a scenario with a large number of requests, this may not scale well.
- Great pool features like connection recycle and test for liveness can not be used.
@hwchase17
@eyurtsev
### 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
1. Configure Postgres to kill idle sessions after 10 seconds (I'm using the `ankane/pgvector` docker image):
```sql
ALTER SYSTEM SET idle_session_timeout TO '10000';
ALTER SYSTEM SET idle_in_transaction_session_timeout TO '10000';
```
2. Restart the database
3. Confirm the new settings are active
```sql
SHOW ALL;
```
4. Create a sample program that instantiate a `PGVector` class and search by similarity every 30 seconds, sleeping between each iteration
```python
if __name__ == '__main__':
# Logging level
logging.basicConfig(level=logging.DEBUG)
load_dotenv()
# PG Vector
driver = os.environ["PGVECTOR_DRIVER"]
host = os.environ["PGVECTOR_HOST"]
port = os.environ["PGVECTOR_PORT"]
database = os.environ["PGVECTOR_DATABASE"]
user = os.environ["PGVECTOR_USER"]
password = os.environ["PGVECTOR_PASSWORD"]
collection_name = "state_of_the_union_test"
connection_string = f'postgresql+{driver}://{user}:{password}@{host}:{port}/{database}'
embeddings = OpenAIEmbeddings()
loader = TextLoader("./state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
chunks = text_splitter.split_documents(documents)
db = PGVector.from_documents(
embedding=embeddings,
documents=chunks,
collection_name=collection_name,
connection_string=connection_string,
)
for i in range(100):
try:
print('--> *** Searching by similarity ***')
result = db.similarity_search_with_score("foo")
print('--> *** Documents retrieved successfully ***')
# for doc, score in result:
# print(f'{score}:{doc.page_content}')
except Exception as e:
print('--> *** Fail ***')
print(str(e))
print('--> *** Sleeping ***')
time.sleep(30)
print('\n\n')
```
5. Confirm in the console output that some requests fail because the idle connection was closed
```
(poc-pgvector-py3.11) ➜ poc-pgvector python -m poc_pgvector
DEBUG:openai:message='Request to OpenAI API' method=post path=https://.../v1/embeddings
DEBUG:urllib3.util.retry:Converted retries value: 2 -> Retry(total=2, connect=None, read=None, redirect=None, status=None)
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): ...:443
DEBUG:urllib3.connectionpool:https://...:443 "POST /v1/embeddings HTTP/1.1" 200 None
DEBUG:openai:message='OpenAI API response' path=https://.../v1/embeddings processing_ms=211 request_id=ec4ef62b-da79-4055-a8da-66fce881a6e9 response_code=200
--> *** Searching by similarity ***
DEBUG:openai:message='Request to OpenAI API' method=post path=https://.../v1/embeddings
DEBUG:openai:api_version=None data='{"input": [[8134]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
DEBUG:urllib3.connectionpool:https://...:443 "POST /v1/embeddings HTTP/1.1" 200 None
DEBUG:openai:message='OpenAI API response' path=https://.../v1/embeddings processing_ms=40 request_id=f2b791ac-0c88-4362-b14c-d6a36084a932 response_code=200
--> *** Documents retrieved successfully ***
--> *** Sleeping ***
--> *** Searching by similarity ***
DEBUG:openai:message='Request to OpenAI API' method=post path=https://.../v1/embeddings
DEBUG:openai:api_version=None data='{"input": [[8134]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
DEBUG:urllib3.connectionpool:https://...:443 "POST /v1/embeddings HTTP/1.1" 200 None
DEBUG:openai:message='OpenAI API response' path=https://.../v1/embeddings processing_ms=30 request_id=de593b87-29ad-4342-81c1-3c495d5b4f56 response_code=200
--> *** Fail ***
(psycopg2.OperationalError) server closed the connection unexpectedly
This probably means the server terminated abnormally
before or while processing the request.
[SQL: SELECT langchain_pg_collection.name AS langchain_pg_collection_name, langchain_pg_collection.cmetadata AS langchain_pg_collection_cmetadata, langchain_pg_collection.uuid AS langchain_pg_collection_uuid
FROM langchain_pg_collection
WHERE langchain_pg_collection.name = %(name_1)s
LIMIT %(param_1)s]
[parameters: {'name_1': 'state_of_the_union_test', 'param_1': 1}]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
--> *** Sleeping ***
--> *** Searching by similarity ***
DEBUG:openai:message='Request to OpenAI API' method=post path=https://.../v1/embeddings
DEBUG:openai:api_version=None data='{"input": [[8134]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
DEBUG:urllib3.connectionpool:https://...:443 "POST /v1/embeddings HTTP/1.1" 200 None
DEBUG:openai:message='OpenAI API response' path=https://.../v1/embeddings processing_ms=42 request_id=e2cb6145-8ceb-4127-b4ee-c5130ba42e8e response_code=200
--> *** Documents retrieved successfully ***
--> *** Sleeping ***
--> *** Searching by similarity ***
DEBUG:openai:message='Request to OpenAI API' method=post path=https://.../v1/embeddings
DEBUG:openai:api_version=None data='{"input": [[8134]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
DEBUG:urllib3.connectionpool:https://...:443 "POST /v1/embeddings HTTP/1.1" 200 None
DEBUG:openai:message='OpenAI API response' path=https://.../v1/embeddings processing_ms=38 request_id=05e15050-0c37-4f7c-a118-a3fafc2bf979 response_code=200
--> *** Fail ***
(psycopg2.OperationalError) server closed the connection unexpectedly
This probably means the server terminated abnormally
before or while processing the request.
[SQL: SELECT langchain_pg_collection.name AS langchain_pg_collection_name, langchain_pg_collection.cmetadata AS langchain_pg_collection_cmetadata, langchain_pg_collection.uuid AS langchain_pg_collection_uuid
FROM langchain_pg_collection
WHERE langchain_pg_collection.name = %(name_1)s
LIMIT %(param_1)s]
[parameters: {'name_1': 'state_of_the_union_test', 'param_1': 1}]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
--> *** Sleeping ***
--> *** Searching by similarity ***
DEBUG:openai:message='Request to OpenAI API' method=post path=https://.../v1/embeddings
DEBUG:openai:api_version=None data='{"input": [[8134]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
DEBUG:urllib3.connectionpool:https://...:443 "POST /v1/embeddings HTTP/1.1" 200 None
DEBUG:openai:message='OpenAI API response' path=https://.../v1/embeddings processing_ms=28 request_id=2deecd8b-42de-42dd-a242-004d9d5811eb response_code=200
--> *** Documents retrieved successfully ***
--> *** Sleeping ***
...
```
### Expected behavior
- Use pooled connections to make feasible executing requests in parallel.
- Allow fine tuning the connection pool used by PGVector, like configuring the its maximum size, number of seconds to wait before giving up on getting a connection from the pool, and enabling [pessimist](https://docs.sqlalchemy.org/en/20/core/pooling.html#pool-disconnects-pessimistic) (liveness testing connections when borrowing them from the pool) or optimistic (connection recycle time) strategies for disconnection dandling.
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PR_kwDOIPDwls5cAQTA
| 11,432 |
Fix PyMUPDFLoader kwargs
|
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[] | null | 1 | 2023-10-05T12:26:38 | 2023-10-05T12:55:43 | 2023-10-05T12:55:43 |
CONTRIBUTOR
| null |
- **Description:** Fix the `PyMuPDFLoader` to accept `loader_kwargs` from the document loader's `loader_kwargs` option. This provides more flexibility in formatting the output documents.
- **Issue:** The `loader_kwargs` is not passed into the `load` method from the document loader, which limits configuration options.
- **Dependencies:** None
|
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Update aws.mdx
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[] | null | 2 | 2023-10-05T12:20:21 | 2023-10-05T16:07:16 | 2023-10-05T16:07:16 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** changed 'configur' to 'configure',
- **Issue:** None,
- **Dependencies:** None,
- **Tag maintainer:** None,
- **Twitter handle:** None
-->
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| 1,928,130,885 |
I_kwDOIPDwls5y7PFF
| 11,430 |
Loading UnstructeredPDF fails after build with PyInstaller
|
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[] | null | 5 | 2023-10-05T12:08:46 | 2023-10-05T20:04:59 | 2023-10-05T20:02:29 |
NONE
| null |
### System Info
Windows 11
Python 3.10
Langchain 0.0.286
Pyinstaller 5.7.0
File "test1.py", line 5, in <module>
File "langchain\document_loaders\unstructured.py", line 86, in load
File "langchain\document_loaders\pdf.py", line 57, in _get_elements
File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module
File "unstructured\partition\pdf.py", line 40, in <module>
File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module
File "unstructured\partition\lang.py", line 3, in <module>
File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module
File "iso639\__init__.py", line 4, in <module>
File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module
File "iso639\language.py", line 12, in <module>
sqlite3.OperationalError: unable to open database file
[19716] Failed to execute script 'test1' due to unhandled exception!
### Who can help?
@agola11
@eyurtsev
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.document_loaders import UnstructuredPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
loader = UnstructuredPDFLoader("C:\\Temp\\AI_experimenting\\indexer_test1\\test_docs\\testdoc1.pdf")
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=20)
docs_transformed = text_splitter.split_documents(data)
print (f'Number of chunks of data: {len(docs_transformed )}')
```
Compile the code above using the following Pyinstaller spec file:
```
# -*- mode: python -*-
import os
from os.path import join, dirname, basename
datas = [
("C:/Temp/AI_experimenting/venv/Lib/site-packages/langchain/chains/llm_summarization_checker/prompts/*.txt", "langchain/chains/llm_summarization_checker/prompts"),
]
googleapihidden = ["pkg_resources.py2_warn",
"googleapiclient",
"apiclient",
"google-api-core"]
# list of modules to exclude from analysis
excludes = []
# list of hiddenimports
hiddenimports = googleapihidden
# binary data
# assets
tocs = []
a = Analysis(['test1.py'],
pathex=[os.getcwd()],
binaries=None,
datas=datas,
hiddenimports=hiddenimports,
hookspath=[],
runtime_hooks=[],
excludes=excludes,
win_no_prefer_redirects=False,
win_private_assemblies=False)
pyz = PYZ(a.pure, a.zipped_data)
exe1 = EXE(pyz,
a.scripts,
name='mytest',
exclude_binaries=True,
icon='',
debug=False,
bootloader_ignore_signals=False,
strip=False,
upx=True,
console=True)
coll = COLLECT(exe1,
a.binaries,
a.zipfiles,
a.datas,
*tocs,
strip=False,
upx=True,
name='mytest_V1')
```
Compile with: pyinstaller --log-level DEBUG test1.spec
Then in cmd terminal change working directory to ....dist\mytest_V1 and run mytest.exe
### Expected behavior
The expected behavior would be an output like:
Number of chunks of data: 18
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PR_kwDOIPDwls5cAEJU
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Nc/5oct/runnable release
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[] | null | 1 | 2023-10-05T11:57:04 | 2023-10-05T13:27:52 | 2023-10-05T13:27:51 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5b_-1K
| 11,427 |
Parse string arguments correctly when using a structured tool
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NONE
| null |
**Description:**
Previously when setting up a structured tool with multiple parameters e.g. `arg1, arg2`
we got a schema validation error by pydantic. The reason for this was simple: Although the required parameters for the tool were correctly identified, they were propagated as string e.g. `'{"arg1": "foo", "arg2": "bar"}'`
This PR fixes this issue by using the `parse_raw` method if the args are of type string
**Issue:**
Didn't find a related issue.
**Dependencies:**
None
**Tag maintainer:**
None
**Twitter handle:**
None
/CC @Depaccu
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I_kwDOIPDwls5y6shA
| 11,426 |
pip installing 'langchain[all]' installs a newer (broken) version than pip install langchain does
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[] | null | 1 | 2023-10-05T10:49:12 | 2023-10-05T11:18:44 | 2023-10-05T11:18:44 |
NONE
| null |
### System Info
I am using Python 3.11.5 and I run into this issue with `ModuleNotFoundError: No module named 'langchain.document_loaders'` after running `pip install 'langchain[all]'`, which appears to be installing langchain-0.0.39
If I then run `pip uninstall langchain`, followed by `pip install langchain`, it proceeds to install langchain-0.0.308 and suddenly my document loaders work again.
- Why does langchain 0.0.39 have document_loaders broken?
- Why does langchain[all] install a different version of langchain?
### Reproduction
1. Install python 3.11.5 (using pyenv on OSX)
2. Run `pip install `langchain[all]'`
3. Observe langchain version that is installed
4. Run `ipython` and execute `from langchain.document_loaders import WebBaseLoader` to receive the module error.
5. Run `pip uninstall langchain`
6. Run `pip install langchain`
7. Note how an older version of langchain is installed
8. Run `ipython` and execute `from langchain.document_loaders import WebBaseLoader` to see how it's working.
### Expected behavior
`pip install langchain` should install the same underlying version of langchain as the version of langchain that includes all of the various modules.
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I_kwDOIPDwls5y6W1a
| 11,425 |
DOC: QA using a Retriever, why do we need additional empty space?
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[] | null | 1 | 2023-10-05T10:09:14 | 2023-10-05T10:21:40 | null |
NONE
| null |
### Issue with current documentation:
In [QA using a Retriever docs](https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_qa) you can observe that after each LLM reply we observe additional empty space. What is the purpose of having this space or is it just a bug in Prompt?
Copied from the docs provided above
```python3
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
```
Provides response:
`" The president said that she is one of the nation's top legal minds ..."`
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5b_D5a
| 11,424 |
integration test for DocAI parser
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[] | null | 2 | 2023-10-05T09:30:21 | 2023-10-05T19:38:30 | 2023-10-05T19:38:30 |
CONTRIBUTOR
| null |
- **Description:** added an integration test
- **Issue:** #11407
@baskaryan
|
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I_kwDOIPDwls5y4rsq
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Issue: Custom Redirect URI as parameter in SharePointLoader
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[] | null | 2 | 2023-10-05T06:16:38 | 2023-10-05T08:38:05 | null |
NONE
| null |
### Issue you'd like to raise.
I am trying to access documents from SharePoint using Langchain's [SharePointLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.sharepoint.SharePointLoader.html).
I changed the redirect URI to a local host on this [documentation](https://python.langchain.com/docs/integrations/document_loaders/microsoft_sharepoint). How can I pass the new redirect URI as a parameter to the [SharePointLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.sharepoint.SharePointLoader.html) function.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5y4iZY
| 11,422 |
SupabaseVectorStore.from_documents is not working
|
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[] | null | 25 | 2023-10-05T05:39:43 | 2023-11-08T06:20:58 | null |
NONE
| null |
### System Info
langchain==0.0.300
supabase==1.1.1
### Who can help?
@hwaking @eyurtsev @agola11 @eyurtsev @hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
### **Creation of Supabase client**
`supabase_url: str = os.environ.get("SUPABASE_URL") # type: ignore
supabase_key: str = os.environ.get("SUPABASE_SERVICE_KEY") # type: ignore
supabase_client = create_client(supabase_url, supabase_key)`
### Text Splitter creation
`text_splitter = CharacterTextSplitter(
chunk_size=800,
chunk_overlap=0,
)`
### **Embeddings**
`embeddings = OpenAIEmbeddings()`
### **Loading the document**
`loader = PyPDFLoader("Alice_in_wonderland2.pdf")
pages = loader.load_and_split()
docs = text_splitter.split_documents(pages)`
### **Save values to Supabase**
`vector_store = SupabaseVectorStore.from_documents(documents=docs, embedding=embeddings, client=supabase_client)`
### **Error encountring**
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "D:\VSCode\Python\langchain project\supabase-try\test.py", line 34, in <module>
vector_store = SupabaseVectorStore.from_documents(
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\langchain\vectorstores\base.py", line 417, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\langchain\vectorstores\supabase.py", line 147, in from_texts
cls._add_vectors(client, table_name, embeddings, docs, ids)
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\langchain\vectorstores\supabase.py", line 323, in _add_vectors
result = client.from_(table_name).upsert(chunk).execute() # type: ignore
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\postgrest\_sync\request_builder.py", line 57, in execute
r = self.session.request(
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\httpx\_client.py", line 814, in request
return self.send(request, auth=auth, follow_redirects=follow_redirects)
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\httpx\_client.py", line 901, in send
response = self._send_handling_auth(
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\httpx\_client.py", line 929, in _send_handling_auth
response = self._send_handling_redirects(
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\httpx\_client.py", line 966, in _send_handling_redirects
response = self._send_single_request(request)
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\httpx\_client.py", line 1002, in _send_single_request
response = transport.handle_request(request)
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\httpx\_transports\default.py", line 218, in handle_request
resp = self._pool.handle_request(req)
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\contextlib.py", line 135, in __exit__
self.gen.throw(type, value, traceback)
File "C:\Users\User\AppData\Local\Programs\Python\Python39\lib\site-packages\httpx\_transports\default.py", line 77, in map_httpcore_exceptions
raise mapped_exc(message) from exc
httpx.WriteTimeout: The write operation timed out
### I tried changing the code according to the langchain docs as
`vector_store = SupabaseVectorStore.from_documents(
docs,
embeddings,
client=supabase_client,
table_name="documents",
query_name="match_documents",
)`
### Then I encountered the following error
2023-10-05 10:33:29,879:INFO - HTTP Request: POST https://scptrclvtrvcwjdunlrn.supabase.co/rest/v1/documents "HTTP/1.1 404 Not Found"
Traceback (most recent call last):
File "D:\VSCode\Python\langchain project\supabase-try\test.py", line 34, in <module>
vector_store = SupabaseVectorStore.from_documents(
**I didnt create the document table in the supabase manually as i need it to be created automatically with the code. And if i need to create it manually i need to know the steps of create that as well and how to integrate it as well. Please help me immediately**
### Expected behavior
SupabaseVectorStore.from_documents works fine and Store all the embeddings in the vector store.
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PR_kwDOIPDwls5b9bw1
| 11,421 |
Update bageldb.py
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[] | null | 1 | 2023-10-05T03:54:13 | 2023-10-05T19:37:56 | 2023-10-05T19:37:56 |
CONTRIBUTOR
| null |
I have restructured the code to ensure uniform handling of ImportError. In place of previously used ValueError, I've adopted the standard practice of raising ImportError with explanatory messages. This modification enhances code readability and clarifies that any problems stem from module importation.
@baskaryan
|
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PR_kwDOIPDwls5b9VDf
| 11,420 |
Upgrade langchain dependencies.
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[] | null | 1 | 2023-10-05T03:09:45 | 2023-10-05T14:57:21 | 2023-10-05T14:57:20 |
COLLABORATOR
| null |
I was hoping this would pick up numpy 1.26, which is required to support the new Python 3.12 release, but it didn't. It seems that some transitive dependency requirement on numpy is preventing that, and the highest we can currently go is 1.24.x.
But to find this out required a 15min `poetry lock`, so I figured we might as well upgrade the dependencies we can and hopefully make the next dependency upgrade a bit smaller.
|
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Wfh/rtds
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[] | null | 1 | 2023-10-05T02:55:09 | 2023-10-12T01:49:16 | 2023-10-12T01:49:16 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,927,197,832 |
PR_kwDOIPDwls5b8_xF
| 11,418 |
feat: add BigFramesLLM integration
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[] | null | 6 | 2023-10-05T00:59:28 | 2023-10-16T21:59:02 | null |
CONTRIBUTOR
| null |
This PR adds [Bigframes (BigQuery DataFrames)](https://cloud.google.com/python/docs/reference/bigframes/latest) integration to Langchain. BigQuery DataFrames provides a Pythonic DataFrame and machine learning (ML) API powered by the BigQuery engine. BigFrames supports manipulating data in the DataFrame to build LLM prompts, and sending DataFrame prompts to the LLM model using [text-bison model of the PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text)
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,927,175,230 |
I_kwDOIPDwls5y3lw-
| 11,416 |
UnstructuredWordDocumentLoader - Missing Percentages (%) characters in data
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[] | null | 2 | 2023-10-05T00:35:06 | 2023-10-05T01:07:51 | null |
NONE
| null |
### System Info
I am using UnstructuredWordDocumentLoader module to load a .docx file. I already tried several things... but the text data returned is always without the percentages that are several times in the .docx file.
How can I maintain / get this (%) percentage symbol?
Thanks
### Who can help?
@hwchase17 @agola11 @eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
loader = UnstructuredWordDocumentLoader("example_data/fake.docx", mode="elements")
data = loader.load()
### Expected behavior
Have in the data the percentages characters also.
|
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PR_kwDOIPDwls5b8q6_
| 11,415 |
Remove overridden async not implemented method on embeddings filters and add default async implementation for document compressors
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[] | null | 2 | 2023-10-04T23:06:05 | 2023-10-05T14:56:04 | 2023-10-05T14:56:03 |
CONTRIBUTOR
| null |
@nfcampos @eyurtsev @baskaryan
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I_kwDOIPDwls5y3Q-2
| 11,414 |
DOC: optional ids issue in delete method in vectorstores
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[] | null | 0 | 2023-10-04T22:50:28 | 2023-10-04T22:52:36 | null |
NONE
| null |
### Issue with current documentation:
In the documentation for some examples like [Weaviate](https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html#langchain.vectorstores.weaviate.Weaviate.delete) and [ElasticsearchVectorStore](https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html#langchain.vectorstores.elasticsearch.ElasticsearchStore.delete), the delete method is described as having the ids parameter as optional.
`delete(ids: Optional[List[str]] = None, **kwargs: Any) → None`
However, the ids parameter is mandatory for those vectorstores, and if it is not provided, a ValueError will be raised with the message "No ids provided to delete." Here is the revised [documentation](https://github.com/langchain-ai/langchain/blob/b9fad28f5e093415d76aeb71b5e555eb87fd2ec2/libs/langchain/langchain/vectorstores/elastic_vector_search.py#L336C5-L348C61) to clarify this:
`
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
# TODO: Check if this can be done in bulk
for id in ids:
self.client.delete(index=self.index_name, id=id)
`
### Idea or request for content:
Please clarify in the documentation
Is it also possible to get bulk delete functionality for some vectorstore (ex. ElasticsearchVectorStore/ Weviate)?
|
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| 1,927,049,240 |
PR_kwDOIPDwls5b8fnH
| 11,413 |
feat: Update Google Document AI Parser
|
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[] | null | 1 | 2023-10-04T22:04:24 | 2023-10-09T15:49:06 | 2023-10-09T15:04:26 |
CONTRIBUTOR
| null |
- **Description:** Code Refactoring, Documentation Improvements for Google Document AI PDF Parser
- Adds Online (synchronous) processing option.
- Adds default field mask to limit payload size.
- Skips Human review by default.
- **Issue:** Fixes #10589
|
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PR_kwDOIPDwls5b8aFU
| 11,412 |
Normalize Option in Scoring Chain
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[] | null | 1 | 2023-10-04T21:43:23 | 2023-10-04T22:59:29 | 2023-10-04T22:59:28 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5b8ZFd
| 11,411 |
feat: add markdown list parser
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[] | null | 5 | 2023-10-04T21:38:42 | 2023-10-06T21:58:16 | 2023-10-06T19:25:46 |
CONTRIBUTOR
| null |
**Description:** add `MarkdownListOutputParser` as a new `ListOutputParser`
**Issue:** #11410
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I_kwDOIPDwls5y2_pu
| 11,410 |
Feature: Markdown list output parser
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[] | null | 1 | 2023-10-04T21:38:14 | 2023-10-07T01:57:03 | 2023-10-07T01:57:03 |
CONTRIBUTOR
| null |
### Feature request
I would like to implement a `ListOutputParser` that could transform Markdown list into a string list
### Motivation
It seems that most of the lists generated by LLMs are Markdown lists, so I think this will be useful.
It might also make it easier to handle cases where the generated list doesn't work with the existing `ListOutputParser` (Numered and CommaSeparated).
### Your contribution
Here is the PR: https://github.com/langchain-ai/langchain/pull/11411
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I_kwDOIPDwls5y29ph
| 11,409 |
Redis from_existing_index does not raise ValueError when index does not exist
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[] | null | 5 | 2023-10-04T21:30:28 | 2023-10-09T15:05:22 | 2023-10-09T15:05:22 |
NONE
| null |
### System Info
langchain 0.0.303, python 3.9, redis-py 5.0.1, latest redis stack with RedisSearch module
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Try to create Redis vector store like this from non existent index_name:
`
embeddings = OpenAIEmbeddings()
vectorstore = Redis.from_existing_index(
embeddings,
index_name=index_name,
schema='schema.yaml',
redis_url=REDIS_URL,
)
`
it executes and returns object of Redis type, and logs that index was found. However, of course it fails to query later.
### Expected behavior
It should raise a ValueError accortding to doc.
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I_kwDOIPDwls5y26fH
| 11,408 |
BooleanOutputParser expected output value error
|
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[] | null | 6 | 2023-10-04T21:18:38 | 2024-01-04T23:13:15 | null |
NONE
| null |
### System Info
Hi, I am using LLMChainFilter.from_llm(llm) but while running, I am getting this error:
ValueError: BooleanOutputParser expected output value to either be YES or NO. Received Yes, the context is relevant to the question as it provides information about the problem in the.
How do I resolve this error?
Langchain version: 0.0.308
### Who can help?
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor, LLMChainFilter
llm = SageMakerEndpointModel
_filter = LLMChainFilter.from_llm(llm)
compressor = LLMChainExtractor.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(base_compressor=_filter, base_retriever=faiss_retriever)
compressed_docs = compression_retriever.get_relevant_documents("What did the president say about Ketanji Jackson Brown?")
### Expected behavior
Get filtered docs
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I_kwDOIPDwls5y2raX
| 11,407 |
Error "InvalidArgument: 400 Request" by following tutorial for Document AI
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[] | null | 17 | 2023-10-04T20:32:22 | 2023-10-30T21:28:38 | null |
NONE
| null |
### System Info
I’m running this on a local machine Windows 10, Spyder 5.2.1 IDE with Anaconda package management, using python 3.10.
### Who can help?
@leo-gan @holtskinner
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hi,
I've just started learning to code with python, working with LLMs and I'm following the [tutorial for setting](https://python.langchain.com/docs/integrations/document_transformers/docai) up LangChain with Google Document AI and I’m getting this error “InvalidArgument: 400 Request contains an invalid argument.” with this line of code
`docs = list(parser.lazy_parse(blob))`
Here are the things I’ve tried so far:
• Setting up gcloud ADC so I can run this as authroized session, code wouldn’t work otherwise
• Set the permission in the GSC bucket to Storage Admin so I can read/write
• Chatgpt wrote test to see if current credentials are working, it is
• Chatgpt wrote test to see if DocAIParser object is working, it is
I think there’s some issue with the output path for “lazy_parse” but I can’t get it to work. I've looked into the documentation but I can't tell if I'm missing something or not. How do I get this working?
See full code and full error message below:
```python
import pprint
from google.auth.transport.requests import AuthorizedSession
from google.auth import default
from google.cloud import documentai
from langchain.document_loaders.blob_loaders import Blob
from langchain.document_loaders.parsers import DocAIParser
PROJECT = "[replace with project name]"
GCS_OUTPUT_PATH = "gs://[replace with bucket path]"
PROCESSOR_NAME = "https://us-documentai.googleapis.com/v1/projects/[replace with processor name]"
# Get the credentials object using ADC.
credentials, _ = default()
session = AuthorizedSession(credentials=credentials)
# Create a Document AI client object.
client = documentai.DocumentProcessorServiceClient(credentials=credentials)
"""Tests if the current credentials are working in gcloud."""
import google.auth
def test_credentials():
try:
# Try to authenticate to the Google Cloud API.
google.auth.default()
print("Credentials are valid.")
except Exception as e:
print("Credentials are invalid:", e)
if __name__ == "__main__":
test_credentials()
import logging
from google.cloud import documentai
# Set up logging
logging.basicConfig(level=logging.DEBUG)
# Create DocumentAI client
client = documentai.DocumentProcessorServiceClient()
# Print out actual method call
logging.debug("Calling client.batch_process_documents(%s, %s)",
PROCESSOR_NAME, GCS_OUTPUT_PATH)
"""Test of DocAIParser object is working"""
# Try to create a DocAIParser object.
try:
parser = DocAIParser(
processor_name=PROCESSOR_NAME,
gcs_output_path=GCS_OUTPUT_PATH,
client=client,
)
# If the DocAIParser object was created successfully, then the Google is accepting the parameters.
print("Google is accepting the parameters.")
except Exception as e:
# If the DocAIParser object fails to be created, then the Google is not accepting the parameters.
print("Google is not accepting the parameters:", e)
parser = DocAIParser(
processor_name=PROCESSOR_NAME,
gcs_output_path=GCS_OUTPUT_PATH,
client=client,
)
blob = Blob(path="gs://foia_doc_bucket/input/goog-exhibit-99-1-q1-2023-19.pdf")
docs = list(parser.lazy_parse(blob))
print(len(docs))
```
**********************************************
Full error message:
```
DEBUG:google.auth._default:Checking None for explicit credentials as part of auth process...
DEBUG:google.auth._default:Checking Cloud SDK credentials as part of auth process...
DEBUG:urllib3.util.retry:Converted retries value: 3 -> Retry(total=3, connect=None, read=None, redirect=None, status=None)
DEBUG:google.auth.transport.requests:Making request: POST https://oauth2.googleapis.com/token
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): oauth2.googleapis.com:443
DEBUG:urllib3.connectionpool:https://oauth2.googleapis.com:443 "POST /token HTTP/1.1" 200 None
Traceback (most recent call last):
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\google\api_core\grpc_helpers.py", line 75, in error_remapped_callable
return callable_(*args, **kwargs)
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\grpc\_channel.py", line 1161, in __call__
return _end_unary_response_blocking(state, call, False, None)
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\grpc\_channel.py", line 1004, in _end_unary_response_blocking
raise _InactiveRpcError(state) # pytype: disable=not-instantiable
_InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.INVALID_ARGUMENT
details = "Request contains an invalid argument."
debug_error_string = "UNKNOWN:Error received from peer ipv4:142.250.31.95:443 {created_time:"2023-10-04T19:56:49.9162929+00:00", grpc_status:3, grpc_message:"Request contains an invalid argument."}"
>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\Inspiron 15 amd 5505\Dropbox\[...]\local_doc_upload.py", line 80, in <module>
docs = list(parser.lazy_parse(blob))
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\langchain\document_loaders\parsers\docai.py", line 91, in lazy_parse
yield from self.batch_parse([blob], gcs_output_path=self._gcs_output_path)
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\langchain\document_loaders\parsers\docai.py", line 122, in batch_parse
operations = self.docai_parse(blobs, gcs_output_path=output_path)
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\langchain\document_loaders\parsers\docai.py", line 268, in docai_parse
operations.append(self._client.batch_process_documents(request))
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\google\cloud\documentai_v1\services\document_processor_service\client.py", line 786, in batch_process_documents
response = rpc(
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\google\api_core\gapic_v1\method.py", line 131, in __call__
return wrapped_func(*args, **kwargs)
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\google\api_core\retry.py", line 366, in retry_wrapped_func
return retry_target(
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\google\api_core\retry.py", line 204, in retry_target
return target()
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\google\api_core\timeout.py", line 120, in func_with_timeout
return func(*args, **kwargs)
File "C:\Users\Anaconda\envs\python3_10\lib\site-packages\google\api_core\grpc_helpers.py", line 77, in error_remapped_callable
raise exceptions.from_grpc_error(exc) from exc
InvalidArgument: 400 Request contains an invalid argument.
```
### Expected behavior
Suppose to output "11" based on the number of pages in [this pdf](https://abc.xyz/assets/a7/5b/9e5ae0364b12b4c883f3cf748226/goog-exhibit-99-1-q1-2023-19.pdf) per the [Doc AI tutorial](https://python.langchain.com/docs/integrations/document_transformers/docai)
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PR_kwDOIPDwls5b8EP3
| 11,406 |
Add docker compose to CLI
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[] | null | 1 | 2023-10-04T20:24:31 | 2023-10-05T14:58:58 | 2023-10-05T14:58:57 |
COLLABORATOR
| null |
Add docker compose to cli
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Issue: prevent a agent from exceeding the token limit
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[] | null | 8 | 2023-10-04T20:11:52 | 2023-11-02T20:49:02 | null |
NONE
| null |
### Issue you'd like to raise.
Hey there, I'm trying got find a way to prevent an agent from going over the token limit, I've looked at the docs, asked around and had no luck.
This is the error for better content:
[ERROR] InvalidRequestError: This model's maximum context length is 16384 tokens. However, your messages resulted in 17822 tokens. Please reduce the length of the messages.
Any help would be great 😊
### Suggestion:
_No response_
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CLI: Readme update
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[] | null | 1 | 2023-10-04T20:07:08 | 2023-10-04T20:25:39 | 2023-10-04T20:25:38 |
COLLABORATOR
| null |
Consolidating to a single README for now, will be easier to maintain we can differentiate between poetry and pip later. Does not seem critical.
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Rm bedrock anthropic error
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COLLABORATOR
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Log a warning instead of raising an error when validating bedrock anthropic prompts. Let server handle actual error behavior.
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PR_kwDOIPDwls5b77u7
| 11,402 |
Fix typing imports in extraction usecase
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CONTRIBUTOR
| null |
The person class here: https://python.langchain.com/docs/use_cases/extraction#pydantic-1 has attributes `dog_breed` and `dog_name` that use `Optional` from typing, but it hasn't been imported. Fixed the import here
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Fix python imports in example,
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5b76Yq
| 11,401 |
Update QA doc w/ Runnables
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[] | null | 1 | 2023-10-04T19:50:38 | 2023-10-05T15:07:40 | 2023-10-05T15:07:39 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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Retriever based on GCP DocAI Warehouse
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CONTRIBUTOR
| null |
- **Description:** implements a retriever on top of DocAI Warehouse (to interact with existing enterprise documents)
https://cloud.google.com/document-ai-warehouse?hl=en
- **Issue:** new functionality
@baskaryan
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PR_kwDOIPDwls5b7z1o
| 11,399 |
mv docs extras
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I_kwDOIPDwls5y2Vgs
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StreamlitCallbackHandler thinking.. / spinner Not stopping
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[] | null | 6 | 2023-10-04T19:30:40 | 2024-01-12T14:53:07 | null |
NONE
| null |
### System Info
python:3.10.13 bookworm (docker)
streamlit
Version: 1.27.1
langchain
Version: 0.0.306
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# Display assistant response in chat message container
with st.chat_message("🧞♂️"):
message_placeholder = st.empty()
cbh = StreamlitCallbackHandler(st.container())
AI_response = llm_chain.run(prompt,callbacks=[cbh])
### Expected behavior
the "Thinking.." spinner STOPS or hides after LLM finishes its response
No Parameters I can find here
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html
that would affect this
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I_kwDOIPDwls5y2P9w
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Retrun_source_documets does not work !!!!
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[] | null | 1 | 2023-10-04T19:13:38 | 2023-10-04T19:20:39 | null |
NONE
| null |
### System Info
"There was a BUG when using return_source_documents = True with any chain, it was always raising an error!!
!!, this is a temporary fix that requires the 'answer' key to be present there, but FIX IT, it is now impossible to to return_source_docs !!!!!!!!!!!!"
def _get_input_output(
self, inputs: Dict[str, Any], outputs: Dict[str, Any]
) -> Tuple[str, str]:
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
if self.output_key is None:
if 'answer' in outputs:
output_key = 'answer'
else:
raise ValueError("Output key 'answer' not found.")
else:
output_key = self.output_key
return inputs[prompt_input_key], outputs[output_key]
### Who can help?
@eyur
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
def create_chain():
retriever = cretae_retriver....
)
memory = memory...
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
retriever=retriever,
verbose=True,
memory=memory,
return_source_documents=True, !!!!!! Here , it does not work
)
return chain
def run_chat():
chain = create_chain()
while True:
query = input("Prompt: ")
if query in ["quit", "q", "exit"]:
sys.exit()
result = chain({"question": query})
print(f"ANSWER: {result['answer']}")
print(f"DOCS_USED: {result['source_documents']}")
query = None
### Expected behavior
It will say this error
raise ValueError(f"One output key expected, got {outputs.keys()}")
ValueError: One output key expected, got dict_keys(['answer', 'source_documents'])
|
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| 1,926,820,740 |
PR_kwDOIPDwls5b7t03
| 11,395 |
mv LCEL up in docs
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| 1,926,819,470 |
PR_kwDOIPDwls5b7tjh
| 11,394 |
Feature/enhance huggingfacepipeline to handle different return type
|
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[] | null | 2 | 2023-10-04T19:10:24 | 2023-10-12T02:09:03 | 2023-10-12T02:09:03 |
CONTRIBUTOR
| null |
**Description:** Avoid huggingfacepipeline to truncate the response if user setup return_full_text as False within huggingface pipeline.
**Dependencies:** : None
**Tag maintainer:** Maybe @sam-h-bean ?
|
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PR_kwDOIPDwls5b7XMQ
| 11,393 |
make vectorstores optional
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I_kwDOIPDwls5y1tLR
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Access Denied Exception while accessing Cross Account Sagemaker endpoints.
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[] | null | 1 | 2023-10-04T17:32:33 | 2023-10-04T17:40:04 | null |
NONE
| null |
### System Info
Langchain: 0.0.308
Python: 3.8
def get_ml_response(self, question):
try:
chain = create_conversation_retrieval_chain()
llm_response = chain({'question': question, "chat_history": []})
logger.info(f"LLm Response: {llm_response}")
except Exception:
err_msg = "Execption! {}".format(traceback.format_exc())
logger.info(err_msg)
def create_conversation_retrieval_chain(self):
try:
session = boto3.Session()
sts_client = session.client("sts")
assumed_role = sts_client.assume_role(RoleArn=role_arn, RoleSessionName="AssumeRoleSession")
# Get the temporary credentials
credentials = assumed_role["Credentials"]
access_key = credentials["AccessKeyId"]
secret_key = credentials["SecretAccessKey"]
session_token = credentials["SessionToken"]
# Configure the default AWS Session with assumed role credentials
# config = Config(region_name="us-west-2")
session = boto3.Session(
aws_access_key_id=access_key,
aws_secret_access_key=secret_key,
aws_session_token=session_token
)
custom_template = """Given the following conversation and a follow up question, rephrase the follow up question
to be a standalone question. At the end of standalone question add this 'Answer the question
in English language.If you do not know the answer reply with 'I am sorry'.
Chat History: {chat_history}
Follow Up Input: {question}
Standalone question:"""
custom_question_prompt = PromptTemplate.from_template(custom_template)
auth = AWSV4SignerAuth(credentials, 'us-west-2', 'aoss')
embeddings = SagemakerEndpointEmbeddingsJumpStart(
endpoint_name="jumpstart-dft-hf-textembedding-all-minilm-l6-v2",
region_name="us-west-2",
content_handler=content_handler_embeddings
)
opensearch_vector_search = OpenSearchVectorSearch(
opensearch_url="<opensearch_url>",
embedding_function=embeddings,
index_name="<index_name>",
http_auth=auth,
connection_class=RequestsHttpConnection,
)
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True,
input_key="question",
output_key="answer",
)
chain = ConversationalRetrievalChain.from_llm(
llm=SagemakerEndpoint(
endpoint_name="jumpstart-dft-hf-llm-falcon-40b-instruct-bf16",
region_name="us-west-2",
content_handler=content_handler_llm,
),
# for experimentation, you can change number of documents to retrieve here
retriever=opensearch_vector_search.as_retriever(
search_kwargs={
"k": 3,
}
),
memory=memory,
condense_question_prompt=custom_question_prompt,
return_source_documents=True,
)
return chain
except Exception:
err_msg = "Execption! {}".format(traceback.format_exc())
logger.info(err_msg)
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
chain = self.create_conversation_retrieval_chain()
llm_response = chain({'question': question, "chat_history": []})
### Expected behavior
I am expecting to connect to connect to Sagemaker endpoints using Assume role permissions but getting Access Denied Exception. I tried invoking the endpoints through the lambda function which is working fine. But not able to figure out how to pass the credentials using Langchain. Could you please help me with this.
|
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PR_kwDOIPDwls5b7J4Q
| 11,391 |
Improve output of Runnable.astream_log()
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[] | null | 1 | 2023-10-04T17:15:01 | 2023-10-04T19:16:38 | 2023-10-04T19:16:37 |
COLLABORATOR
| null |
- Make logs a dictionary keyed by run name (and counter for repeats)
- Ensure no output shows up in lc_serializable format
- Fix up repr for RunLog and RunLogPatch
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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PR_kwDOIPDwls5b7JMS
| 11,390 |
Make langchain compatible with SQLAlchemy<1.4.0
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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## Description
Currently SQLAlchemy >=1.4.0 is a hard requirement. We are unable to run `from langchain.vectorstores import FAISS` with SQLAlchemy <1.4.0 due to top-level imports, even if we aren't even using parts of the library that use SQLAlchemy. See Testing section for repro. Let's make it so that langchain is still compatible with SQLAlchemy <1.4.0, especially if we aren't using parts of langchain that require it.
The main conflict is that SQLAlchemy removed `declarative_base` from `sqlalchemy.ext.declarative` in 1.4.0 and moved it to `sqlalchemy.orm`. We can fix this by try-catching the import. This is the same fix as applied in https://github.com/langchain-ai/langchain/pull/883.
(I see that there seems to be some refactoring going on about isolating dependencies, e.g. https://github.com/langchain-ai/langchain/commit/c87e9fb2ce0ae617e3b2edde52421c80adef54cc, so if this issue will be eventually fixed by isolating imports in langchain.vectorstores that also works).
## Issue
I can't find a matching issue.
## Dependencies
No additional dependencies
## Maintainer
@hwchase17 since you reviewed https://github.com/langchain-ai/langchain/pull/883
## Testing
I didn't add a test, but I manually tested this.
1. Current failure:
```
langchain==0.0.305
sqlalchemy==1.3.24
```
``` python
python -i
>>> from langchain.vectorstores import FAISS
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/pay/src/zoolander/vendor3/lib/python3.8/site-packages/langchain/vectorstores/__init__.py", line 58, in <module>
from langchain.vectorstores.pgembedding import PGEmbedding
File "/pay/src/zoolander/vendor3/lib/python3.8/site-packages/langchain/vectorstores/pgembedding.py", line 10, in <module>
from sqlalchemy.orm import Session, declarative_base, relationship
ImportError: cannot import name 'declarative_base' from 'sqlalchemy.orm' (/pay/src/zoolander/vendor3/lib/python3.8/site-packages/sqlalchemy/orm/__init__.py)
```
2. This fix:
```
langchain==<this PR>
sqlalchemy==1.3.24
```
``` python
python -i
>>> from langchain.vectorstores import FAISS
<succeeds>
```
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PR_kwDOIPDwls5b7HoA
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Add cohere /chat integration
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[] | null | 2 | 2023-10-04T17:07:38 | 2023-10-05T16:20:48 | 2023-10-05T16:20:48 |
CONTRIBUTOR
| null |
Add cohere /chat integration and an iPython notebook to demonstrate the addition.
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PR_kwDOIPDwls5b7E4J
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docs: `integrations/retrievers` cleanup
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[] | null | 1 | 2023-10-04T16:58:39 | 2023-10-06T22:04:11 | 2023-10-06T20:40:46 |
COLLABORATOR
| null |
fixed several notebooks:
- headers
- formats
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I_kwDOIPDwls5y1fvn
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Sources are not returned in RetrievalQAWithSourcesChain
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[] | null | 1 | 2023-10-04T16:54:58 | 2023-10-04T17:02:06 | null |
NONE
| null |
### System Info
I'm using Google Colab, authenticating with my own account.
Packages installation:
!pip install google-cloud-discoveryengine google-cloud-aiplatform langchain==0.0.236 -q
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Sample code:
import vertexai
from langchain.llms import VertexAI
from langchain.retrievers import GoogleCloudEnterpriseSearchRetriever
vertexai.init(project=PROJECT_ID, location=REGION)
llm = VertexAI(model_name=MODEL, temperature=0, top_p=0.2, max_output_tokens=1024)
retriever = GoogleCloudEnterpriseSearchRetriever(
project_id=PROJECT_ID, search_engine_id=DATA_STORE_ID
)
from langchain.chains import RetrievalQAWithSourcesChain
search_query = "How to create a new google calendar?"
retrieval_qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type(
llm=llm, chain_type="stuff", retriever=retriever
)
results = retrieval_qa_with_sources({"question": search_query})
print(f"Answer: {results['answer']}")
print(f"Sources: {results['sources']}")
Return:
Answer: To create a new calendar, follow these steps:
1. On the computer, open Google Calendar.
2. On the left, next to "Other calendars," click Add other calendars->Create new calendar.
3. Add a name and description to the calendar.
4. Click on Create a Schedule.
5. To share the calendar, click on it in the left bar and select Share with specific people.
Sources:
### Expected behavior
Expected to have the sources as URI from Google Cloud Storage. It sometimes return, sometimes not. It is really random.
|
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| 1,926,617,079 |
I_kwDOIPDwls5y1df3
| 11,386 |
Issue: similarity_score_threshold not working with pinecone as expected
|
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[] | null | 1 | 2023-10-04T16:48:26 | 2023-10-05T14:32:34 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I am using Lang chain version 0.0.307 and I noticed an issue with the similarity_score_threshold. I just upgraded from 0.0.208 or 288 (I forgot).
This is the code I am using to retrieve the documents from pinecone:
```
docsearch = Pinecone.from_existing_index(index, embeddings, text_key="text")
retriever = docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={
'score_threshold': 0.6
}
)
retriever_docs_result = retriever.get_relevant_documents(query)
```
it used to work fine but after upgrading the langchain version it stopped returning any docs. Upon investigating the code, I've noticed that inside the `_similarity_search_with_relevance_scores` method in `vectorstore.py` , `docs_and_scores` is returning the scores as expected but then this code tries to calculate the relevance score and inverts the values:
`return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]`.
I am getting search score values as 0.7 to 0.85 but then the relevancy score inverts it and makes it (1-score) which is around 0.3 to 0.15. The problem is the score_threshold works the opposite way. So if I set score_threshold to 0.6 it wants scores bigger than 0.6 which is impossible because the relevancy score makes it invert (if the relevancy score is smaller it means it is more closer to what we queried).
I am using dot product and I have used the following code as well but it also didn't help:
`
docsearch = Pinecone(index, embeddings, text_key="text", distance_strategy=DistanceStrategy.MAX_INNER_PRODUCT)
retriever = docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={
'score_threshold': 0.6
}
)
`
Can someone help or I am missing something here ?
### Suggestion:
_No response_
|
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https://github.com/langchain-ai/langchain/pull/11385
| 1,926,579,942 |
PR_kwDOIPDwls5b65uq
| 11,385 |
Add optional input_types to prompt template
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[] | null | 2 | 2023-10-04T16:23:06 | 2023-10-04T17:54:55 | 2023-10-04T17:54:54 |
COLLABORATOR
| null |
- default MessagesPlaceholder one to list of messages
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,926,558,948 |
I_kwDOIPDwls5y1PTk
| 11,384 |
Issue: TypeError: TextGenerationModel.predict() got an unexpected keyword argument 'stop_sequences'
|
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[] | null | 2 | 2023-10-04T16:10:08 | 2023-10-18T00:25:51 | null |
NONE
| null |
### Issue you'd like to raise.
I got this error when I built an ChatBot with Langchain using VertexAI. I'm seeing this error and couldn't find any details so far.
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/conversational_retrieval/base.py", line 141, in _call
answer = self.combine_docs_chain.run(
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 492, in run
return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py", line 105, in _call
output, extra_return_dict = self.combine_docs(
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 171, in combine_docs
return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/llm.py", line 257, in predict
return self(kwargs, callbacks=callbacks)[self.output_key]
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/llm.py", line 93, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/llm.py", line 103, in generate
return self.llm.generate_prompt(
File "/opt/conda/lib/python3.10/site-packages/langchain/llms/base.py", line 504, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/langchain/llms/base.py", line 668, in generate
new_results = self._generate_helper(
File "/opt/conda/lib/python3.10/site-packages/langchain/llms/base.py", line 541, in _generate_helper
raise e
File "/opt/conda/lib/python3.10/site-packages/langchain/llms/base.py", line 528, in _generate_helper
self._generate(
File "/opt/conda/lib/python3.10/site-packages/langchain/llms/vertexai.py", line 281, in _generate
res = completion_with_retry(
File "/opt/conda/lib/python3.10/site-packages/langchain/llms/vertexai.py", line 102, in completion_with_retry
return _completion_with_retry(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "/opt/conda/lib/python3.10/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "/opt/conda/lib/python3.10/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
File "/opt/conda/lib/python3.10/concurrent/futures/_base.py", line 451, in result
return self.__get_result()
File "/opt/conda/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/opt/conda/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/langchain/llms/vertexai.py", line 100, in _completion_with_retry
return llm.client.predict(*args, **kwargs)
TypeError: TextGenerationModel.predict() got an unexpected keyword argument 'stop_sequences'
### Suggestion:
_No response_
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