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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://api.github.com/repos/langchain-ai/langchain/issues/12700
|
https://api.github.com/repos/langchain-ai/langchain
|
https://api.github.com/repos/langchain-ai/langchain/issues/12700/labels{/name}
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https://api.github.com/repos/langchain-ai/langchain/issues/12700/comments
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https://github.com/langchain-ai/langchain/issues/12700
| 1,971,998,242 |
I_kwDOIPDwls51ik4i
| 12,700 |
UnstructuredFileLoader does not work so I am using UnstructuredFileIOLoader
|
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"id": 5347462,
"node_id": "MDQ6VXNlcjUzNDc0NjI=",
"avatar_url": "https://avatars.githubusercontent.com/u/5347462?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/ZahraBoroujeni",
"html_url": "https://github.com/ZahraBoroujeni",
"followers_url": "https://api.github.com/users/ZahraBoroujeni/followers",
"following_url": "https://api.github.com/users/ZahraBoroujeni/following{/other_user}",
"gists_url": "https://api.github.com/users/ZahraBoroujeni/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ZahraBoroujeni/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ZahraBoroujeni/subscriptions",
"organizations_url": "https://api.github.com/users/ZahraBoroujeni/orgs",
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[
{
"id": 5541144676,
"node_id": "LA_kwDOIPDwls8AAAABSkcoZA",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/area:%20doc%20loader",
"name": "area: doc loader",
"color": "D4C5F9",
"default": false,
"description": "Related to document loader module (not documentation)"
},
{
"id": 5680700839,
"node_id": "LA_kwDOIPDwls8AAAABUpidpw",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/auto:bug",
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open
| false | null |
[] | null | 1 | 2023-11-01T09:18:50 | 2023-11-01T09:30:23 | null |
NONE
| null |
### System Info
Langchain version 0.0.327, WSL ubuntu 22, python version 3.10.3
As you can see in the code below the UnstructuredFileLoader does not work and can not load the file.
```
from langchain.document_loaders import UnstructuredFileLoader
from langchain.document_loaders import UnstructuredFileIOLoader
# UnstructuredFileLoader does not work so I am using UnstructuredFileIOLoader
loader = UnstructuredFileLoader('data/scenario/xodr/common_junction_creator.xodr',mode="single", strategy="fast")
documents_xml = loader.load() #
print(documents_xml)
print(f"Number of documents={len(documents_xml)}")
with open("data/scenario/xodr/road_with_custom_roadmarkers.xodr", "rb") as f:
loader = UnstructuredFileIOLoader(
f, mode="single", strategy="fast",
)
docs = loader.load()
print(docs)
print(f"Number of documents={len(docs)}")
```
Output:
**[Document(page_content='', metadata={'source': 'data/scenario/xodr/common_junction_creator.xodr'})]
Number of documents=1**
[Document(page_content='<OpenDRIVE>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<header name="road with custom lanes" revMajor="1" revMinor="5" date="2023-10-24 13:01:55.839866" north="0.0" south="0.0" east="0.0" west="0.0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': 'f2bcadaa39170fe65d413bbd3b2dc2bb', 'languages': ['eng'], 'category': 'UncategorizedText'}), Document(page_content='<road rule="RHT" id="0" junction="-1" length="300">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<link/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<planView>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<geometry s="0" x="0" y="0" hdg="0" length="300">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': 'db34f8689a2ee8ac79294c4b6daccc9b', 'languages': ['eng'], 'category': 'UncategorizedText'}), Document(page_content='<line/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</geometry>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</planView>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<elevationProfile/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<lateralProfile/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<lanes>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<laneSection s="0">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<left>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': 'aa1aca19f288f19fc4ea835d72dff88e', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<lane id="1" type="driving" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': 'aa1aca19f288f19fc4ea835d72dff88e', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<link/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<width a="4" b="0" c="0" d="0" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'UncategorizedText'}), Document(page_content='<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</left>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<center>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<lane id="0" type="none" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<roadMark sOffset="0" type="solid solid" weight="standard" color="standard" height="0.02">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<type name="solid solid" width="None">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<line length="0" space="0" tOffset="0.2" width="0.2" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<line length="0" space="0" tOffset="-0.2" width="0.2" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</type>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</roadMark>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</center>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<right>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<lane id="-1" type="driving" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '1b93bbf1878690ead63ce7f1619e042e', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<link/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<width a="4" b="0" c="0" d="0" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'UncategorizedText'}), Document(page_content='<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</right>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</laneSection>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<laneSection s="100">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<left>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': 'b3712cf5f4c02aadc160326bedfe5ba6', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<lane id="1" type="driving" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': 'b3712cf5f4c02aadc160326bedfe5ba6', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<link/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<width a="4" b="0" c="0" d="0" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'UncategorizedText'}), Document(page_content='<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</left>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<center>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<lane id="0" type="none" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<roadMark sOffset="0" type="solid broken" weight="standard" color="standard" height="0.02">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<type name="solid broken" width="None">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<line length="0" space="0" tOffset="0.2" width="0.2" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<line length="3" space="3" tOffset="-0.2" width="0.2" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</type>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</roadMark>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</center>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<right>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<lane id="-1" type="driving" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '1b93bbf1878690ead63ce7f1619e042e', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<link/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<width a="4" b="0" c="0" d="0" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'UncategorizedText'}), Document(page_content='<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</right>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</laneSection>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<laneSection s="200">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<left>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '970aed3aa631e4aac64ea6da5ec910ad', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<lane id="1" type="driving" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '970aed3aa631e4aac64ea6da5ec910ad', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<link/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<width a="4" b="0" c="0" d="0" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'UncategorizedText'}), Document(page_content='<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</left>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<center>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<lane id="0" type="none" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<roadMark sOffset="0" type="broken broken" weight="standard" color="standard" height="0.02">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '17412d8e1360a85c15cb6702ea423aa4', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<type name="broken broken" width="None">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '17412d8e1360a85c15cb6702ea423aa4', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<line length="9" space="3" tOffset="0.2" width="0.2" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<line length="3" space="9" tOffset="-0.2" width="0.2" sOffset="3"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</type>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</roadMark>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</center>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<right>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<lane id="-1" type="driving" level="false">', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '1b93bbf1878690ead63ce7f1619e042e', 'languages': ['eng'], 'category': 'NarrativeText'}), Document(page_content='<link/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='<width a="4" b="0" c="0" d="0" sOffset="0"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'parent_id': '9e49db703ffb69458886d0d4db951017', 'languages': ['eng'], 'category': 'UncategorizedText'}), Document(page_content='<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</lane>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</right>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</laneSection>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</lanes>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</road>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'}), Document(page_content='</OpenDRIVE>', metadata={'last_modified': '2023-10-30T08:42:44', 'filetype': 'text/plain', 'languages': ['eng'], 'category': 'Title'})]
Number of documents=93
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. run the python code for an openDrive file (.xodr) file.
```
<?xml version="1.0" encoding="UTF-8"?>
<OpenDRIVE>
<header name="my_road" revMajor="1" revMinor="5" date="2023-10-24 12:47:51.670385" north="0.0" south="0.0" east="0.0" west="0.0"/>
<road rule="RHT" id="1" junction="-1" length="100">
<link>
<successor elementType="junction" elementId="100"/>
</link>
<planView>
<geometry s="0" x="0" y="0" hdg="0" length="100">
<line/>
</geometry>
</planView>
<elevationProfile/>
<lateralProfile/>
<lanes>
<laneSection s="0">
<left>
<lane id="2" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
<lane id="1" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</left>
<center>
<lane id="0" type="none" level="false">
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</center>
<right>
<lane id="-1" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
<lane id="-2" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</right>
</laneSection>
</lanes>
</road>
<road rule="RHT" id="2" junction="-1" length="100">
<link>
<predecessor elementType="junction" elementId="100"/>
</link>
<planView>
<geometry s="0" x="149.99999999999957" y="50.000000000000895" hdg="1.5706573464102072" length="100">
<line/>
</geometry>
</planView>
<elevationProfile/>
<lateralProfile/>
<lanes>
<laneSection s="0">
<left>
<lane id="1" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</left>
<center>
<lane id="0" type="none" level="false">
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</center>
<right>
<lane id="-1" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</right>
</laneSection>
</lanes>
</road>
<road rule="RHT" id="3" junction="-1" length="100">
<link>
<predecessor elementType="junction" elementId="100"/>
</link>
<planView>
<geometry s="0" x="200.0" y="0.0" hdg="9.26535897924907e-05" length="100">
<line/>
</geometry>
</planView>
<elevationProfile/>
<lateralProfile/>
<lanes>
<laneSection s="0">
<left>
<lane id="2" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
<lane id="1" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</left>
<center>
<lane id="0" type="none" level="false">
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</center>
<right>
<lane id="-1" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
<lane id="-2" type="driving" level="false">
<link/>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</right>
</laneSection>
</lanes>
</road>
<road rule="RHT" id="100" junction="100" length="100.00000012397831">
<link>
<predecessor elementType="road" elementId="1" contactPoint="end"/>
<successor elementType="road" elementId="3" contactPoint="start"/>
</link>
<planView>
<geometry s="0" x="100.0" y="0.0" hdg="0" length="33.33333335241042">
<spiral curvStart="1e-09" curvEnd="-2.7801076883213167e-06"/>
</geometry>
<geometry s="33.33333335241042" x="133.33333334526168" y="-0.0005144643872362006" hdg="-4.6318461498530586e-05" length="33.33333341915747">
<spiral curvStart="-2.780107688321317e-06" curvEnd="5.558715377723275e-06"/>
</geometry>
<geometry s="66.66666677156789" x="166.66666672388249" y="-0.0020586908846665537" hdg="-8.333222595482807e-09" length="33.33333335241042">
<spiral curvStart="5.5587153777232755e-06" curvEnd="9.999999999996516e-10"/>
</geometry>
</planView>
<elevationProfile/>
<lateralProfile/>
<lanes>
<laneSection s="0">
<left>
<lane id="2" type="driving" level="false">
<link>
<predecessor id="2"/>
<successor id="2"/>
</link>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
<lane id="1" type="driving" level="false">
<link>
<predecessor id="1"/>
<successor id="1"/>
</link>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</left>
<center>
<lane id="0" type="none" level="false">
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</center>
<right>
<lane id="-1" type="driving" level="false">
<link>
<predecessor id="-1"/>
<successor id="-1"/>
</link>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="broken" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
<lane id="-2" type="driving" level="false">
<link>
<predecessor id="-2"/>
<successor id="-2"/>
</link>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</right>
</laneSection>
</lanes>
</road>
<road rule="RHT" id="101" junction="100" length="80.57849294213008">
<link>
<predecessor elementType="road" elementId="1" contactPoint="end"/>
<successor elementType="road" elementId="2" contactPoint="start"/>
</link>
<planView>
<geometry s="0" x="100.0" y="3.0" hdg="0" length="25.406829175061056">
<spiral curvStart="1e-09" curvEnd="0.02541075466216959"/>
</geometry>
<geometry s="25.406829175061056" x="125.14335899689604" y="5.713523057106356" hdg="0.32280336415897914" length="29.764834592007983">
<spiral curvStart="0.0254107546621696" curvEnd="0.031526347239798955"/>
</geometry>
<geometry s="55.171663767069035" x="146.64343936963272" y="24.998132686495545" hdg="1.1701650742891778" length="25.406829175061056">
<spiral curvStart="0.03152634723979895" curvEnd="9.999999994736442e-10"/>
</geometry>
</planView>
<elevationProfile/>
<lateralProfile/>
<lanes>
<laneSection s="0">
<left>
<lane id="1" type="driving" level="false">
<link>
<predecessor id="2"/>
<successor id="1"/>
</link>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</left>
<center>
<lane id="0" type="none" level="false">
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</center>
</laneSection>
</lanes>
</road>
<road rule="RHT" id="102" junction="100" length="80.58464104902598">
<link>
<predecessor elementType="road" elementId="2" contactPoint="start"/>
<successor elementType="road" elementId="3" contactPoint="start"/>
</link>
<planView>
<geometry s="0" x="149.99999999999957" y="50.000000000000895" hdg="4.71225" length="25.408049844246484">
<spiral curvStart="1e-09" curvEnd="0.03153901640632509"/>
</geometry>
<geometry s="25.408049844246484" x="153.35124601428106" y="24.99636057548867" hdg="5.112922463149233" length="29.768541360533018">
<spiral curvStart="0.0315390164063251" curvEnd="0.025406434592294906"/>
</geometry>
<geometry s="55.1765912047795" x="174.85483977842026" y="5.710997601779319" hdg="5.9605139698225456" length="25.408049844246484">
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</geometry>
</planView>
<elevationProfile/>
<lateralProfile/>
<lanes>
<laneSection s="0">
<left>
<lane id="1" type="driving" level="false">
<link>
<predecessor id="-1"/>
<successor id="2"/>
</link>
<width a="3" b="0" c="0" d="0" sOffset="0"/>
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</left>
<center>
<lane id="0" type="none" level="false">
<roadMark sOffset="0" type="solid" weight="standard" color="standard" height="0.02" width="0.2"/>
</lane>
</center>
</laneSection>
</lanes>
</road>
<junction name="my_junction" id="100" type="default">
<connection incomingRoad="3" id="0" contactPoint="end" connectingRoad="100">
<laneLink from="2" to="2"/>
<laneLink from="1" to="1"/>
<laneLink from="-1" to="-1"/>
<laneLink from="-2" to="-2"/>
</connection>
<connection incomingRoad="1" id="1" contactPoint="start" connectingRoad="100">
<laneLink from="2" to="2"/>
<laneLink from="1" to="1"/>
<laneLink from="-1" to="-1"/>
<laneLink from="-2" to="-2"/>
</connection>
<connection incomingRoad="1" id="2" contactPoint="start" connectingRoad="101">
<laneLink from="2" to="1"/>
</connection>
<connection incomingRoad="2" id="3" contactPoint="start" connectingRoad="102">
<laneLink from="-1" to="1"/>
</connection>
</junction>
</OpenDRIVE>
```
### Expected behavior
Fix the bug in the library.
|
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| 12,698 |
DOC: BaseCallbackHandler vs BaseTracer
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[] | null | 1 | 2023-11-01T07:27:08 | 2023-11-01T07:38:20 | null |
NONE
| null |
### Issue with current documentation:
While reviewing the document, I am learning about the callback functionality of langchain and have a question.
In langchain.callbacks, there are two classes: `BaseCallbackHandler` and `BaseTracer`.
From what I can see in the document, it seems like the roles of these two classes are similar.
Could you please provide an explanation of the differences between these two classes and clarify when it is appropriate to inherit from each class when creating a custom callback?
### Idea or request for content:
_No response_
|
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Issue: Pinecone Vector Store incorrectly assigns score in similarity_search_with_relevance_scores
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[] | null | 1 | 2023-11-01T06:51:09 | 2023-11-01T07:01:28 | null |
NONE
| null |
### Issue you'd like to raise.
With the Pinecone index setup with `cosine` metric, so the scores received from the vector database are already in the`cosine` metric.
However, the `_select_relevance_score_fn` of langchain pinecone vector store returns the base vector store's `_cosine_relevance_score_fn` for the `cosine` distance strategy.
https://github.com/langchain-ai/langchain/blob/b2138508cbf17aef3a08cdce072d79fd5dc63dc3/libs/langchain/langchain/vectorstores/pinecone.py#L231
In the base vector store implementation of `_cosine_relevance_score_fn` the score is the complement.
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/schema/vectorstore.py#L187
So the cosine metric of 0.81 returned by the pinecone vector database indicating a higher similarity is turned into 0.19 in langchain and processed as very low similarity.
### Suggestion:
_No response_
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Keep also original query - multi_query.py
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[] | null | 2 | 2023-11-01T05:37:52 | 2023-11-09T11:20:29 | 2023-11-03T01:15:02 |
CONTRIBUTOR
| null |
When you use a MultiQuery it might be useful to use the original query as well as the newly generated ones to maximise the changes to retriever the correct document. I haven't created an issue, it seems a very small and easy thing.
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| 12,695 |
ChatTongyi class use _generate method as async call and result in replying text dispeared
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[] | null | 7 | 2023-11-01T05:20:30 | 2023-11-13T14:38:00 | null |
NONE
| null |
### System Info
langchain 0.0.327
### 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
it is easy to reproduction
use AsyncIteratorCallbackHandler() to get ChatTongyi stream data,
it only get warning:
`/venv/lib/python3.10/site-packages/langchain/chat_models/tongyi.py:365: RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited
run_manager.on_llm_new_token(chunk.content, chunk=chunk)`
and the reply text dispeared
I look into the code , and find the problem is in the _agenerate() with only using _generate()
we can see, the stream functionality in _generate() is incomplete and problematic. One of them is the questionable support for asynchronous callback,
and this is where error happens
it should use _astream()
pls, fix it , thx
### Expected behavior
return stream data
|
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COLLABORATOR
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I_kwDOIPDwls51hV_y
| 12,693 |
DOC: RetrievalQA.from_chain_type batch inference with elastic vectorstore
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[] | null | 2 | 2023-11-01T04:26:22 | 2023-11-10T10:24:40 | null |
NONE
| null |
### Issue with current documentation:
I have searched for a while but there is no example for RetrievalQA batch inference. For example, here is my code:
`model_name = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
embeddings = HuggingFaceEmbeddings(model_name=model_name)
db = ElasticVectorSearch(
embedding = embeddings,
elasticsearch_url="http://localhost:9200",
index_name="test_index",
)
model = transformers.AutoModelForCausalLM.from_pretrained('vilm/vietcuna-7b-v3')
generate_text = transformers.pipeline(
model=model,
tokenizer=tokenizer,
batch_size = 8,
return_full_text=True,
task='text-generation',
stopping_criteria=stopping_criteria,
temperature=0,
max_new_tokens=512,
repetition_penalty=1.1
)
model_id = 'vilm/vietcuna-7b-v3'
llm = HuggingFacePipeline(pipeline=generate_text)
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db.as_retriever(),
verbose=True,
return_source_documents=True,
)
`
then I tried:
`qa.batch(inputs=[{'query':'Who is the prime minister of Vietnam'},{'query':'what is desease?'}])`
and
`qa(inputs=[{'query':'Who is the prime minister of Vietnam'},{'query':'what is desease?'}])`
Both failed. I saw the reason is ElasticVectorSearch cannot return two source documents at the same time. But the main problem is that is there any way to get RetrievalQA do batch inferencing with GPU utilization. LLM batch inference is easy to use (defined params) but it seems like the RetrievalQA chain hasn't supported this.
### Idea or request for content:
_No response_
|
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reopened
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https://api.github.com/repos/langchain-ai/langchain/issues/12691
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| 1,971,553,239 |
PR_kwDOIPDwls5eSmh5
| 12,691 |
Fix typo highlighted by `ruff` autoformatter.
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[] | null | 1 | 2023-11-01T01:54:20 | 2023-11-01T02:16:07 | 2023-11-01T02:16:06 |
COLLABORATOR
| null |
H/t @MichaReiser for spotting it: https://github.com/langchain-ai/langchain/pull/12585/files#r1378253045
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PR_kwDOIPDwls5eSly9
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zep: VectorStore: Use Native MMR
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[] | null | 1 | 2023-11-01T01:50:10 | 2023-11-02T23:45:42 | 2023-11-02T23:45:42 |
CONTRIBUTOR
| null |
- refactor to use Zep's native MMR; update example
-
@baskaryan @eyurtsev
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I_kwDOIPDwls51g1s8
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Missing 'Action:' after 'Thought:
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[] | null | 2 | 2023-11-01T01:41:04 | 2023-11-02T02:54:21 | null |
NONE
| null |
### System Info
langchain==0.0.326
Python 3.10.8
llm = Bedrock
Note: I tested this with the Bedrock LLM and these 3 models:
anthropic.claude-instant-v1
anthropic.claude-v2
ai21.j2-ultra-v1
The error does NOT occur when using llm=OpenAI
EDIT:
Can only replicate this error with anthropic.claude-instant-v1
Stack Trace:
```
Thought:Traceback (most recent call last):
File "...venv\lib\site-packages\langchain\agents\agent.py", line 933, in _take_next_step
output = self.agent.plan(
File "...venv\lib\site-packages\langchain\agents\agent.py", line 547, in plan
return self.output_parser.parse(full_output)
File "...venv\lib\site-packages\langchain\agents\mrkl\output_parser.py", line 61, in parse
raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: ` Fumio Kishida is 65 years old. The largest prime number smaller than 65 is 61.`
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "...langchain\l101.py", line 32, in <module>
agent.run("Who is the current leader of Japan and how old are they? What is the largest prime number that is smaller than the leader's age?")
File "...venv\lib\site-packages\langchain\chains\base.py", line 505, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "...venv\lib\site-packages\langchain\chains\base.py", line 310, in __call__
raise e
File "...venv\lib\site-packages\langchain\chains\base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "...venv\lib\site-packages\langchain\agents\agent.py", line 1146, in _call
next_step_output = self._take_next_step(
File "...venv\lib\site-packages\langchain\agents\agent.py", line 944, in _take_next_step
raise ValueError(
ValueError: An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Could not parse LLM output: ` Fumio Kishida is 65 years old. The largest prime number smaller than 65 is 61.`
```
### Who can help?
@sbusso
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
llm = Bedrock(
credentials_profile_name="default",
model_id="anthropic.claude-instant-v1",
# streaming=True,
model_kwargs={"temperature": 0.0}
)
# llm = OpenAI(temperature=0)
tools = load_tools(["serpapi"],llm=llm)
# agent = initialize_agent(tools=tools,llm=llm,agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,handle_parsing_errors=True,verbose=True)
agent = initialize_agent(tools=tools,llm=llm,agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,verbose=True)
agent.run("Who is the current leader of Japan and how old are they? What is the largest prime number that is smaller than the leader's age?")
```
### Expected behavior
The text should have been parsed. Based on the stack trace:
Could not parse LLM output: ` Fumio Kishida is 65 years old. The largest prime number smaller than 65 is 61.`
I'm thinking this is probably derived from the boto3 API response, because it works fine with OpenAI.
|
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CONTRIBUTOR
| null |
@baskaryan @hwchase17
|
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PR_kwDOIPDwls5eSfK9
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Add possibility to pass on_artifacts for a specific conversation
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[] | null | 1 | 2023-11-01T01:10:19 | 2023-11-06T15:29:47 | 2023-11-06T15:29:47 |
CONTRIBUTOR
| null |
Possibility to pass on_artifacts to a conversation. It can be then achieved by adding this way:
```python
result = agent.run(
input=message.text,
metadata={
"on_artifact": CALLBACK_FUNCTION
},
)
```
<!-- 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_kwDOIPDwls5eSYqx
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Zep: Summary Search and Example
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[] | null | 1 | 2023-11-01T00:34:56 | 2023-11-02T23:31:11 | 2023-11-02T23:31:11 |
CONTRIBUTOR
| null |
Zep now has the ability to search over chat history summaries. This PR adds support for doing so. More here: https://blog.getzep.com/zep-v0-17/
@baskaryan @eyurtsev
|
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properly increment version in cli
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[] | null | 1 | 2023-11-01T00:21:22 | 2023-11-01T00:27:45 | 2023-11-01T00:27:44 |
COLLABORATOR
| null |
Went from 0.0.9 -> 0.0.11 without releasing. Back to 10, then release.
|
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| 1,971,452,662 |
PR_kwDOIPDwls5eSRmr
| 12,684 |
Add RAG input types
|
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COLLABORATOR
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PR_kwDOIPDwls5eSP7r
| 12,683 |
RAG on clinical trial data
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[] | null | 2 | 2023-10-31T23:40:42 | 2023-12-12T01:33:09 | null |
COLLABORATOR
| null |
RAG on records from [clinical trials .gov](https://classic.clinicaltrials.gov/api/gui/ref/download_all).
|
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| 1,971,426,921 |
PR_kwDOIPDwls5eSMOW
| 12,682 |
fix elastic rag template in playground
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[] | null | 1 | 2023-10-31T23:14:48 | 2023-11-01T00:18:36 | 2023-11-01T00:18:35 |
COLLABORATOR
| null |
- a few instructions in the readme (load_documents -> ingest.py)
- added docker run command for local elastic
- adds input type definition to render playground properly
|
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I_kwDOIPDwls51gTWN
| 12,681 |
Local var ref in PALChain.from_math_prompt
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[] | null | 1 | 2023-10-31T22:45:41 | 2023-10-31T22:59:21 | null |
NONE
| null |
### System Info
Python 3.10.12
langchain [v0.0.327](https://github.com/langchain-ai/langchain/releases/tag/v0.0.327)
### Who can help?
@agola
### 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
<img width="909" alt="image" src="https://github.com/langchain-ai/langchain/assets/18315/ed391f1c-fda6-45bb-9ca6-002e99eb6782">
### Expected behavior
The right answer?
|
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PR_kwDOIPDwls5eSEM7
| 12,680 |
link to templates
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[] | null | 1 | 2023-10-31T22:31:00 | 2023-10-31T23:19:23 | 2023-10-31T23:19:22 |
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PR_kwDOIPDwls5eR12n
| 12,679 |
Use black to lint notebooks and docs for now.
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[] | null | 1 | 2023-10-31T21:23:07 | 2023-10-31T21:51:06 | 2023-10-31T21:51:05 |
COLLABORATOR
| null |
Due to #12677 having lots of errors for the time being.
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PR_kwDOIPDwls5eRySk
| 12,678 |
Use an in-project virtualenv in the CLI package.
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[] | null | 1 | 2023-10-31T21:09:12 | 2023-10-31T21:51:25 | 2023-10-31T21:51:24 |
COLLABORATOR
| null |
Keeping it in sync with how our other packages are configured.
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| 1,971,303,319 |
PR_kwDOIPDwls5eRxjg
| 12,677 |
Lint Python notebooks with ruff.
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[] | null | 7 | 2023-10-31T21:06:21 | 2023-11-14T20:58:23 | 2023-11-14T20:58:22 |
COLLABORATOR
| null |
The new ruff version fixed the blocking bugs, and I was able to fairly easily us to a passing state: ruff fixed some issues on its own, I fixed a handful by hand, and I added a list of narrowly-targeted exclusions for files that are currently failing ruff rules that we probably should look into eventually.
I went pretty lenient on the docs / cookbooks rules, allowing dead code and such things. Perhaps in the future we may want to tighten the rules further, but this is already a good set of checks that found real issues and will prevent them going forward.
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PR_kwDOIPDwls5eRut2
| 12,676 |
Both lint and format `templates` with ruff v0.1.3.
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[] | null | 1 | 2023-10-31T20:56:00 | 2023-10-31T21:52:02 | 2023-10-31T21:52:01 |
COLLABORATOR
| null |
- Both lint and format code in `templates`.
- Upgrade to ruff v0.1.3.
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| 1,971,272,370 |
PR_kwDOIPDwls5eRqrQ
| 12,675 |
Remove the CLI package's pydantic compatibility tests.
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[] | null | 1 | 2023-10-31T20:45:20 | 2023-10-31T20:57:39 | 2023-10-31T20:57:38 |
COLLABORATOR
| null |
They aren't necessary, since the CLI package doesn't have a direct dependency on pydantic.
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PR_kwDOIPDwls5eRpi5
| 12,673 |
fix plate chain
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PR_kwDOIPDwls5eRpRj
| 12,672 |
Use `ruff` for both linting and formatting in `langchain-cli`.
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[] | null | 1 | 2023-10-31T20:41:49 | 2023-10-31T20:52:27 | 2023-10-31T20:52:26 |
COLLABORATOR
| null |
Prior to this PR, `ruff` was used only for linting and not for formatting, despite the names of the commands. This PR makes it be used for both linting code and autoformatting it.
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PR_kwDOIPDwls5eRjGK
| 12,671 |
Use separate jobs for building and publishing test releases.
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[] | null | 1 | 2023-10-31T20:20:30 | 2023-11-01T01:36:27 | 2023-11-01T01:36:26 |
COLLABORATOR
| null |
This follows the principle of least privilege. Our `poetry build` step doesn't need, and shouldn't get, access to our GitHub OIDC capability.
This is the same structure as I used in the already-merged PR for refactoring the regular PyPI release workflow: #12578.
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| 1,971,173,088 |
PR_kwDOIPDwls5eRVUc
| 12,668 |
Only publish to test PyPI from the `_test_release.yml` workflow.
|
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[] | null | 1 | 2023-10-31T19:33:10 | 2023-11-01T01:36:39 | 2023-11-01T01:36:39 |
COLLABORATOR
| null |
PyPI trusted publishing wants to know which workflow is expected to do the publish. We always want to publish from the same workflow, so we're making `_test_release.yml` the only workflow that publishes to Test PyPI.
|
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I_kwDOIPDwls51fVpq
| 12,667 |
output parsed: Error: Failed to parse. Text: "10,0,0,....". Error: TypeError: text.trim is not a function
|
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[] | null | 1 | 2023-10-31T19:16:56 | 2023-11-01T12:26:20 | 2023-11-01T12:26:20 |
NONE
| null |
### System Info
I have this method that is trying to format the output of LLM:
```typescript
private async getOrderedTitles(titles: string[], question: string) {
const outputParser = StructuredOutputParser.fromZodSchema(z.array(z.number().describe("The score for this title")));
const llm = new OpenAI({ modelName: "gpt-3.5-turbo", openAIApiKey: this.openAIApiKey, temperature: 0 });
const orderingChain = new LLMChain({
llm,
outputParser,
prompt: new PromptTemplate({
template: getSortingPromot(),
inputVariables: ["question", "titles"],
partialVariables: { format_instructions: outputParser.getFormatInstructions() },
}),
});
const res = await orderingChain.call({ question, titles });
return outputParser.parse(res.text);
}
```
and the prompt is:
```
Give each title a score as to how relevant it is to this question: {question}
##########################################
Titles:
{titles}
##########################################
{format_instructions}
```
it fails with:
```
Error: Failed to parse. Text: "10,0,0,10,0,10,10,0,10,0,10,0,10,0,0". Error: TypeError: text.trim is not a function
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Above code is all that is needed to recreate the issue.
### Expected behavior
There is no obvious reason why it should be failiing
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PR_kwDOIPDwls5eRJIl
| 12,666 |
Add with score functions to MMR for chroma
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| false | null |
[] | null | 3 | 2023-10-31T18:53:19 | 2023-12-27T20:11:28 | null |
NONE
| null |
## 📝 **Description**
This PR introduces two new functions to the Chroma vectorstore:
max_marginal_relevance_search_by_vector_with_scores
max_marginal_relevance_search_with_scores
These functions extend the existing Maximal Marginal Relevance (MMR) functionality. In addition to returning the most relevant documents, they also return their associated similarity scores. This added context enables further customization and is particularly useful for applications requiring more advanced sorting or filtering algorithms.
## 🎯 **Usecase**
The extended functionality will be especially beneficial for applications that require advanced sorting or filtering mechanisms. My specific use-case involves using a Reciprocal Rank Fusion (RRF) system across multiple queries on the same topic. The inclusion of similarity scores alongside documents facilitates this kind of advanced application.
## 🛠 **Dependencies**
None. This feature does not introduce any new dependencies.
## 👥 **Maintainers**
@rlancemartin, @eyurtsev, I would highly appreciate your feedback on these additions.
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| 1,971,072,114 |
PR_kwDOIPDwls5eQ_fv
| 12,665 |
fix for `YahooFinanceNewsTool`
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[] | null | 1 | 2023-10-31T18:23:43 | 2023-10-31T22:00:21 | 2023-10-31T21:58:09 |
COLLABORATOR
| null |
Added YahooFinanceNewsTool to the __init__.py
It was missed here.
|
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VertexAIEmbeddings is raising timout exception
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[] | null | 2 | 2023-10-31T17:31:54 | 2023-11-03T08:59:32 | 2023-11-03T08:59:31 |
NONE
| null |
### System Info
`[tool.poetry.dependencies]
python = "=3.8.16"
langchain = "^0.0.327"
google-cloud-aiplatform = "^1.35.0"
`
code:
`embedding = VertexAIEmbeddings()`
error:
`Traceback (most recent call last): File "/Users/astripathy/Library/Caches/pypoetry/virtualenvs/nssauto-test-2ktKcOZb-py3.8/lib/python3.8/site-packages/google/api_core/grpc_helpers.py", line 75, in error_r
emapped_callable
return callable_(*args, **kwargs) File "/Users/astripathy/Library/Caches/pypoetry/virtualenvs/nssauto-test-2ktKcOZb-py3.8/lib/python3.8/site-packages/grpc/_channel.py", line 1161, in __call__
return _end_unary_response_blocking(state, call, False, None)
File "/Users/astripathy/Library/Caches/pypoetry/virtualenvs/nssauto-test-2ktKcOZb-py3.8/lib/python3.8/site-packages/grpc/_channel.py", line 1004, in _end_unary_response_blocking
raise _InactiveRpcError(state) # pytype: disable=not-instantiable
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with: status = StatusCode.UNAVAILABLE
details = "DNS resolution failed for us-central1-aiplatform.googleapis.com:443: C-ares status is not ARES_SUCCESS qtype=A name=us-central1-aiplatform.googleapis.co
m is_balancer=0: DNS query cancelled" debug_error_string = "UNKNOWN:DNS resolution failed for us-central1-aiplatform.googleapis.com:443: C-ares status is not ARES_SUCCESS qtype=A name=us-central1-aipla
tform.googleapis.com is_balancer=0: DNS query cancelled {grpc_status:14, created_time:"2023-10-31T22:38:41.657524+05:30"}"`
I don't think there is any connectivity issue as I am able to successfully run below query from the same machine:
`MODEL_ID="textembedding-gecko"
PROJECT_ID=XXXX-XXXX
curl \
-X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/publishers/google/models/${MODEL_ID}:predict -d \
$'{
"instances": [
{ "content": "What is life?"}
],
}'
{
"predictions": [
{
"embeddings": {
"values": [
0.010562753304839134,
0.049150310456752777,`
### Who can help?
@hwchase17 @agola11
### Information
- [X] 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`!pip install langchain
!pip install google-cloud-aiplatform
import langchain
from langchain.embeddings import VertexAIEmbeddings
from google.cloud import aiplatform
embedding = VertexAIEmbeddings()`
### Expected behavior
Below code should not throw any exception:
` embedding = VertexAIEmbeddings()`
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PR_kwDOIPDwls5eQjDX
| 12,661 |
Update README for Hybrid Search Weaviate
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[] | null | 1 | 2023-10-31T17:24:20 | 2023-10-31T18:02:35 | 2023-10-31T18:02:35 |
CONTRIBUTOR
| null |
- **Description:** Updated the README for Hybrid Search Weaviate
|
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PR_kwDOIPDwls5eQfv2
| 12,660 |
use http.client instead of urllib3
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[] | null | 1 | 2023-10-31T17:17:07 | 2023-11-01T18:15:07 | 2023-11-01T18:15:06 |
COLLABORATOR
| null |
dep problems with requests
cloudflare debugging not worth it with urllib
|
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I_kwDOIPDwls51ed0i
| 12,659 |
DOC: When using LCEL, how do I set verbose=True on a chain?
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[] | null | 3 | 2023-10-31T17:11:43 | 2023-10-31T19:56:08 | 2023-10-31T19:56:08 |
NONE
| null |
### Issue with current documentation:
I´m using https://python.langchain.com/docs/guides/debugging and https://python.langchain.com/docs/modules/chains/document/refine.
Somehow the documentation does not line up. How can I set verbose=True on a chain when using LCEL?
### Idea or request for content:
Add documentation on how to activate verbose logs on a chain when using LCEL.
|
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PR_kwDOIPDwls5eQZhN
| 12,658 |
Overwrite existing distributions when uploading to test PyPI.
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PR_kwDOIPDwls5eQVrH
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Update MosaicML Embedding Input Key
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[] | null | 2 | 2023-10-31T16:49:52 | 2023-10-31T18:43:31 | 2023-10-31T18:43:30 |
CONTRIBUTOR
| null |
This input key was missed in the last update PR: https://github.com/langchain-ai/langchain/pull/7391
The input/output formats are intended to be like this:
```
{"inputs": [<prompt>]}
{"outputs": [<output_text>]}
```
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I_kwDOIPDwls51eQtC
| 12,656 |
MultiLanguageModel: work with multiple LLM with the interface of a single one
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[] | null | 9 | 2023-10-31T16:39:54 | 2023-11-20T21:11:44 | null |
CONTRIBUTOR
| null |
### Feature request
The idea is to have an object that can be used in the same way as `LanguageModel` objects, but the object would contain a list of `LanguageModels` that could be chosen according to rules.
For instance I would to call my list of LLM with a round robin strategy in order to distribute requests among multiple LLM.
Example:
```
llm1 = OpenAI(key1)
llm2 = OpenAI(key2)
muti_llm = MultiLLM([llm1, llm2], method="round-robin")
multi_llm.predict(...) # will use llm1
multi_llm.predict(...) # the second time will use the llm2
```
### Motivation
The motivations behind these features are:
- leveraging multi account to avoid API limits per account
- act as a load balancer to easily create balancing strategies (round robin, random, least connection, ...)
- (in the future maybe) have the possibility to make shadowing between multiple LLM or other strategies
### Your contribution
I started working on that but I'm not sure about the object hierarchy I need to implement to achieve this goal properly without impacting too much the existing codebase.
Here is my naive implementation:
```python
LanguageModelInput = TypeVar("LanguageModelOutput")
class BaseMultiLanguageModel(BaseLanguageModel[LanguageModelInput], ABC):
language_models: List[BaseLanguageModel] = Field(
description="List of language models",
min_items=1
)
@property
@abstractmethod
def active_model(self) -> BaseLanguageModel:
...
def generate_prompt(self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any) -> LLMResult:
return self.active_model.generate_prompt(prompts, stop, callbacks, **kwargs)
async def agenerate_prompt(self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Callbacks = None, **kwargs: Any) -> LLMResult:
return await self.active_model.agenerate_prompt(prompts, stop, callbacks, **kwargs)
def predict(self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) -> str:
return self.active_model.predict(text, stop=stop, **kwargs)
def predict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) -> BaseMessage:
return self.active_model.predict_messages(messages, stop=stop, **kwargs)
async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) -> str:
return await self.active_model.apredict(text, stop=stop, **kwargs)
async def apredict_messages(self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) -> BaseMessage:
return await self.active_model.apredict_messages(messages, stop=stop, **kwargs)
def invoke(self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any) -> Output:
return self.active_model.invoke(input, config, **kwargs)
# Example of subclasses creation
class RandomMultiChatModel(BaseMultiLanguageModel[PromptValue]):
@property
def active_model(self) -> BaseLanguageModel:
choice = randint(0, len(self.language_models) - 1)
print(f"Using LLM: {choice}")
return self.language_models[choice]
class RoundRobinMultiChatModel(BaseMultiLanguageModel[PromptValue]):
index: int = 0
@property
def active_model(self) -> BaseLanguageModel:
self.index = (self.index + 1) % len(self.language_models)
print(f"Using LLM: {self.index}")
return self.language_models[self.index]
# Usage
llm1 = ChatOpenAI(openai_api_key=OPENAI_API_KEY_1)
llm2 = ChatOpenAI(openai_api_key=OPENAI_API_KEY_2)
round_robin_multi_llm = RoundRobinMultiChatModel(language_models=[llm1, llm2])
round_robin_multi_llm.predict("Hello, how are you?") # use the same methods as BaseLanguageModel
```
This is working with simple examples but I'm not sure this inheritance hierarchy is the best so any help would be appreciated to define the best solution :)
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| 12,655 |
update lc version
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[] | null | 1 | 2023-10-31T16:39:19 | 2023-10-31T17:19:17 | 2023-10-31T17:19:16 |
COLLABORATOR
| null |
also updated py version in `csv-agent` and `rag-codellama-fireworks` because they have stricter python requirements
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PR_kwDOIPDwls5eQRsg
| 12,654 |
Support release-testing packages with dashes in their names.
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PR_kwDOIPDwls5eQPqJ
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Create autocomment.yml
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[] | null | 9 | 2023-10-31T16:34:04 | 2023-10-31T21:57:03 | 2023-10-31T21:57:03 |
NONE
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I have created a Workflow that automatically commits when an issue is opened, a pull request is created, an issue is closed, or an issue is assigned or unassigned.
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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.
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PR_kwDOIPDwls5eQIqA
| 12,651 |
Add RAG template for Timescale Vector
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[] | null | 1 | 2023-10-31T16:16:33 | 2023-10-31T16:56:31 | 2023-10-31T16:56:30 |
COLLABORATOR
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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
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2. an example notebook showing its use. It lives in `docs/extras` directory.
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PR_kwDOIPDwls5eQHR_
| 12,650 |
fix template py verisons
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PR_kwDOIPDwls5eQHJ6
| 12,649 |
Rename Template
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[] | null | 1 | 2023-10-31T16:12:51 | 2023-10-31T16:15:31 | 2023-10-31T16:15:30 |
COLLABORATOR
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To chatbot feedback. Update import
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PR_kwDOIPDwls5eQClG
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Remove `print()` statements which seemed leftover from debugging.
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[] | null | 1 | 2023-10-31T16:01:15 | 2023-10-31T17:45:49 | 2023-10-31T17:45:48 |
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Added in #12159 presumably during debugging. Right now they cause a bit of visual noise.
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PR_kwDOIPDwls5eQBGg
| 12,647 |
Show ruff output inline in GitHub PRs.
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template updates
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| 12,645 |
MRKL agent is passing "Observation" text to tools when using non-OpenAI LLMs and breaks structured input tools
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[] | null | 6 | 2023-10-31T15:55:50 | 2024-01-04T11:44:18 | null |
NONE
| null |
### System Info
Python 3.9.16
Langchain 0.0.326
Linux (Fedora 37)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.utilities import SerpAPIWrapper
from langchain.agents import AgentType, initialize_agent
from langchain.tools import Tool
import langchain
langchain.debug = True
from langchain.llms import OpenAI
llm = OpenAI(temperature=0, verbose=True)
search = SerpAPIWrapper()
events_tool = Tool(
name="events_tool_serp",
description="A tool to look up current events",
func=search.run,
)
tools = [events_tool]
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What happened yesterday?")
```
When the above code is run with OpenAI, things work OK. However, when swapped for a different LLM endpoint:
```python
# using IBM watsonx.ai
from model_context import get_watsonx_predictor
llm = get_watsonx_predictor(
model="codellama/codellama-34b-instruct", min_new_tokens=5, verbose=True
)
```
Odd things happen:
```
[llm/start] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:LangChainInterface] Entering LLM run with input:
{
"prompts": [
"Answer the following questions as best you can. You have access to the following tools:\n\nevents_tool_serp: A tool to look up current events\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [events_tool_serp]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: What happened yesterday?\nThought:"
]
}
[llm/end] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:LangChainInterface] [2.65s] Exiting LLM run with output:
{
"generations": [
[
{
"text": " I should look up current events\nAction: events_tool_serp\nAction Input: yesterday\nObservation:",
"generation_info": {
"generated_token_count": 25,
"input_token_count": 160,
"stop_reason": "STOP_SEQUENCE",
"stop_sequence": "\nObservation:",
"input_text": "Answer the following questions as best you can. You have access to the following tools:\n\nevents_tool_serp: A tool to look up current events\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [events_tool_serp]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: What happened yesterday?\nThought:"
},
"type": "Generation"
}
]
],
"llm_output": {
"token_usage": {
"generated_token_count": 25,
"input_token_count": 160
}
},
"run": null
}
[chain/end] [1:chain:AgentExecutor > 2:chain:LLMChain] [2.65s] Exiting Chain run with output:
{
"text": " I should look up current events\nAction: events_tool_serp\nAction Input: yesterday\nObservation:"
}
[tool/start] [1:chain:AgentExecutor > 4:tool:events_tool_serp] Entering Tool run with input:
"yesterday
Observation:"
```
The `Observation:` text is being passed into the tool. For simple tools like search engines, this often is not a problem. However, when your tool expects a structured input, you sometimes end up with tool input like the following:
```
"foo,bar
Observation:"
```
This results in the tool attempting to do something like splitting on `,` and then the second part of the resulting array is:
```
bar
Observation:
```
Which definitely breaks the tool.
### Expected behavior
The `Observation:` text should definitely not be passed as part of the tool input.
The MRKL output parser is here:
https://github.com/langchain-ai/langchain/blob/v0.0.327/libs/langchain/langchain/agents/mrkl/output_parser.py#L26-L77
The regex does appear to match the groups correctly:
https://regex101.com/r/v7Tg1n/1
It's unclear, though, why the `Observation:` text is ending up getting passed into the input.
Although, when you use newline characters (`\n`) in the string, and not actual newlines, it does appear that the `Observation` part is being caught in the group:
https://regex101.com/r/MMCvYn/1
I tried to spelunk into the code to figure out when the agent "base" calls `parse` to see why the `Observation` text would be getting passed into parse in the first place, but it seems that one possible fix here would be to modify the parse RegEx for MRKL to make sure it does NOT capture the observation component.
Changing the RegEx in the MRKL agent to the following:
```python
r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)Observation:"
```
And then modifying the tool input strip:
```python
if action_match:
action = action_match.group(1).strip()
action_input = action_match.group(2)
tool_input = action_input.strip()
```
fixes the issue if "Observation" got passed in, but I am assuming that `parse` shouldn't have gotten this in the first place.
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| 1,970,776,750 |
PR_kwDOIPDwls5eP-Iy
| 12,644 |
Update Vertex template
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I_kwDOIPDwls51d5op
| 12,643 |
`llm.batch` method fails with `max_concurrency` in config
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[] | null | 1 | 2023-10-31T15:49:39 | 2023-11-21T20:56:52 | 2023-11-21T20:56:52 |
NONE
| null |
### System Info
- langchain version: 0.0.320
- python: 3.9.17
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Similar to the [guide](https://python.langchain.com/docs/expression_language/interface#batch) for `chat_models.batch`, but if we use `llm` instead, `batch` and `abatch` methods will throw exceptions.
Here is an example for `batch`
```py
model = OpenAI()
prompt = PromptTemplate.from_template("tell me a joke about {topic}")
chain = prompt | model
chain.batch([{"topic": "bears"}, {"topic": "cats"}], config={"max_concurrency": 5})
```
You get the following error for dead loop exceeding recursion limit
```py
...
--> 300 for output in self.batch(
301 batch, config=config, return_exceptions=return_exceptions, **kwargs
302 )
303 ]
File [/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/llms/base.py:273](https://file+.vscode-resource.vscode-cdn.net/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/llms/base.py:273), in BaseLLM.batch(self, inputs, config, return_exceptions, **kwargs)
270 if not inputs:
271 return []
--> 273 config = get_config_list(config, len(inputs))
274 max_concurrency = config[0].get("max_concurrency")
276 if max_concurrency is None:
File [/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/schema/runnable/config.py:115](https://file+.vscode-resource.vscode-cdn.net/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/schema/runnable/config.py:115), in get_config_list(config, length)
108 def get_config_list(
109 config: Optional[Union[RunnableConfig, List[RunnableConfig]]], length: int
110 ) -> List[RunnableConfig]:
111 """
112 Helper method to get a list of configs from a single config or a list of
113 configs, useful for subclasses overriding batch() or abatch().
114 """
--> 115 if length < 0:
116 raise ValueError(f"length must be >= 0, but got {length}")
117 if isinstance(config, list) and len(config) != length:
RecursionError: maximum recursion depth exceeded in comparison
```
Or if you set `max_concurrency` to be smaller than the length of the prompts, say 1
```py
chain.batch([{"topic": "bears"}, {"topic": "cats"}], config={"max_concurrency": 1})
ValueError Traceback (most recent call last)
...
File [/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/schema/runnable/base.py:1271](https://file+.vscode-resource.vscode-cdn.net/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/schema/runnable/base.py:1271), in RunnableSequence.batch(self, inputs, config, return_exceptions, **kwargs)
1269 else:
1270 for i, step in enumerate(self.steps):
-> 1271 inputs = step.batch(
1272 inputs,
1273 [
1274 # each step a child run of the corresponding root run
1275 patch_config(
1276 config, callbacks=rm.get_child(f"seq:step:{i+1}")
1277 )
1278 for rm, config in zip(run_managers, configs)
1279 ],
1280 )
1282 # finish the root runs
1283 except BaseException as e:
File [/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/llms/base.py:297](https://file+.vscode-resource.vscode-cdn.net/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/llms/base.py:297), in BaseLLM.batch(self, inputs, config, return_exceptions, **kwargs)
292 else:
293 batches = [
294 inputs[i : i + max_concurrency]
295 for i in range(0, len(inputs), max_concurrency)
296 ]
--> 297 return [
298 output
299 for batch in batches
300 for output in self.batch(
301 batch, config=config, return_exceptions=return_exceptions, **kwargs
302 )
303 ]
File [/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/llms/base.py:300](https://file+.vscode-resource.vscode-cdn.net/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/llms/base.py:300), in <listcomp>(.0)
292 else:
293 batches = [
294 inputs[i : i + max_concurrency]
295 for i in range(0, len(inputs), max_concurrency)
296 ]
297 return [
298 output
299 for batch in batches
--> 300 for output in self.batch(
301 batch, config=config, return_exceptions=return_exceptions, **kwargs
302 )
303 ]
File [/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/llms/base.py:273](https://file+.vscode-resource.vscode-cdn.net/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/llms/base.py:273), in BaseLLM.batch(self, inputs, config, return_exceptions, **kwargs)
270 if not inputs:
271 return []
--> 273 config = get_config_list(config, len(inputs))
274 max_concurrency = config[0].get("max_concurrency")
276 if max_concurrency is None:
File [/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/schema/runnable/config.py:118](https://file+.vscode-resource.vscode-cdn.net/usr/local/Caskroom/miniconda/base/envs/chatgpt/lib/python3.9/site-packages/langchain/schema/runnable/config.py:118), in get_config_list(config, length)
116 raise ValueError(f"length must be >= 0, but got {length}")
117 if isinstance(config, list) and len(config) != length:
--> 118 raise ValueError(
119 f"config must be a list of the same length as inputs, "
120 f"but got {len(config)} configs for {length} inputs"
121 )
123 return (
124 list(map(ensure_config, config))
125 if isinstance(config, list)
126 else [patch_config(config, copy_locals=True) for _ in range(length)]
127 )
ValueError: config must be a list of the same length as inputs, but got 2 configs for 1 inputs
```
Similar errors for `abatch` with `max_concurrecy`.
I believe the bug lies in https://github.com/langchain-ai/langchain/blob/f94e24dfd79ccf55a14641b6940a706674c070f8/libs/langchain/langchain/llms/base.py#L297 where `config` list length is inconsistent with the `inputs` length after chopping up by max_concurrency. The fix could be as simple as
```
config = {**config[0], "max_concurrency": None} # no list is needed
```
### Expected behavior
No error, just like with `chat_models`
|
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| 1,970,738,037 |
PR_kwDOIPDwls5eP1o4
| 12,642 |
Minor updates to READMEs
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[] | null | 1 | 2023-10-31T15:32:09 | 2023-10-31T15:34:47 | 2023-10-31T15:34:46 |
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PR_kwDOIPDwls5eP1mx
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Publish new versions to test PyPI on package version bump.
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[] | null | 3 | 2023-10-31T15:32:04 | 2023-11-13T22:17:13 | null |
COLLABORATOR
| null |
When a PR updates the `pyproject.toml` version to a number that does not currently exist on PyPI (the main one, not test), a successful test run will cause the package to be built and published on test PyPI under that version by triggering the test release workflow.
We use caching to avoid spamming the PyPI metadata APIs on every PR, and instead only make one request per new version.
We have updated the release workflow to always overwrite test PyPI publishes, so I decided to not make the test publishes use `rc` versions to avoid introducing additional implementation complexity. If you feel that the extra complexity is worth it, I'm happy to switch to generating an `rc` release instead.
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Skip test-publishing `langchain-cli` to test PyPI.
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Relax python version and remove need for explicit setup step
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[] | null | 1 | 2023-10-31T14:42:45 | 2023-11-16T09:01:08 | 2023-10-31T16:42:28 |
CONTRIBUTOR
| null |
This PR addresses what seems like a unnecessary Python version restriction in the pyroject.toml specs within both Cassandra (/Astra DB) templates. With "^3.11" I got some version incompatibilities with the latest "langchain add [...]" commands, so these are now relaxed in line with the other templates I could inspect.
Incidentally, in the "entomology" template, the need for an explicit "setup" step for the user to carry on has been removed, replaced by a check-and-execute-if-necessary instruction on app startup.
Thank you for your attention!
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I_kwDOIPDwls51dRwf
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Langchain Information Extraction
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[] | null | 6 | 2023-10-31T14:32:45 | 2024-01-08T20:30:26 | null |
NONE
| null |
I am trying to use Langchain information extraction chain with OpenAI. Firstly, I am reading a PDF file having some text about products or product. There is no specefic format of PDF, it can be in any format like, there can be only one product on one page or one product can be on two pages or there can be 10 products on one page. So it's completely random. How can I extract the the useful information (some key attributes) for all the products. Currently, I am using Pydantic to validate the output schema and I am using `PyPDFLoader` to load the pdf and then using the `load_and_split`.
```
loader = PyPDFLoader(file_path=file_path)
pages = loader.load_and_split()
```
And then I am looping on the pages like this:
```
llm = ChatOpenAI(temperature=0, model=model_name, openai_api_key=key)
chain = create_extraction_chain(schema, llm)
for index in range(0, len(pages)):
output = chain.run(pages[index])
results.append(output)
```
How can I run the extraction in such a way that it should be completely automated, currently, it is just extracting information from one page and then other and then other.
|
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| 1,970,604,472 |
I_kwDOIPDwls51dQm4
| 12,635 |
Cannot keep history of Cypher LLM in GraphCypherQAChain in the memory object
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[] | null | 2 | 2023-10-31T14:30:34 | 2023-11-01T06:11:25 | 2023-11-01T06:11:25 |
NONE
| null |
### System Info
Python 3.9.16, langchain==0.0.322
### Who can help?
@agola11 @sudranga
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I want to keep generated cypher queries in the memory to feed into Question/Answer LLM which leads better understanding of the context. Following example helps to reproduce error (I am not providing prompt details etc.).
* I initialize a ConversationBufferMemory to keep both question-answer as well as generated cypher queries.
```python
graph = get_neo4j_graph(neo4j_url, neo4j_db_name, neo4j_username, neo4j_password)
custom_cypher_prompt = generate_custom_cypher_prompt()
custom_qa_prompt = generate_custom_qa_prompt()
# First I did not give any input key to Memory but it gives error when there is multiple input in the prompt template
# 'question' input_key makes it work for cypher_llm and qa_llm but fails in GraphCypherQAChain
# Since GraphCypherQAChain default input_key is 'query'
memory = ConversationBufferMemory(memory_key="chat_history", input_key='question')
readonlymemory = ReadOnlySharedMemory(memory=memory)
graph_chain = GraphCypherQAChain.from_llm(
graph=graph,
cypher_llm=ChatOpenAI(temperature='0', model='gpt-4', openai_api_key=openai_api_key),
qa_llm=ChatOpenAI(temperature='0.5', model='gpt-3.5-turbo-16k', openai_api_key=openai_api_key),
cypher_llm_kwargs={"prompt": custom_cypher_prompt, "memory": memory, "verbose": True},
qa_llm_kwargs={"prompt": custom_qa_prompt, "memory": readonlymemory, "verbose": True},
# Limit the number of results from the Cypher QA Chain using the top_k parameter
top_k=5,
# Return intermediate steps from the Cypher QA Chain
# return_intermediate_steps=True,
validate_cypher=True,
verbose=True,
memory=memory
)
response = graph_chain.run(query="<question>")
```
### Expected behavior
Keep generated cypher query in the memory to feed into QA LLM as history.
|
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| 1,970,599,702 |
PR_kwDOIPDwls5ePXVC
| 12,634 |
Fix small typo on Founcational -> Router notebook
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[] | null | 1 | 2023-10-31T14:28:19 | 2023-10-31T16:16:30 | 2023-10-31T16:16:29 |
CONTRIBUTOR
| null |
- **Description:** Fix small typo on Founcational -> Router notebook
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| 1,970,593,919 |
PR_kwDOIPDwls5ePWFG
| 12,633 |
Add retry to ConfluenceLoader
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[] | null | 1 | 2023-10-31T14:25:35 | 2023-10-31T14:34:58 | null |
NONE
| null |
**Description:**
Add retry to ConfluenceLoader
**Issue:**
When an HTTP error occurs during assessing if a page is public all the already an error was raised (e.g. "Rate Limit") although a simple retry could have lead to successful loading of the document(s).
**Dependencies:**
None
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I_kwDOIPDwls51c6Ln
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Add an optional get_token function argument to AzureChatOpenAI to be called on retry
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[] | null | 0 | 2023-10-31T13:46:03 | 2023-10-31T13:48:14 | null |
NONE
| null |
### Feature request
Hi there,
Thanks you so much for this awesome library!
I have a suggestion that might improve the AzureChatOpenAI class.
In my company we use AzureChatOpenAI where the initialization of a `chat` object looks like this:
```
os.environ["OPENAI_API_KEY"] = get_token().token
return AzureChatOpenAI(
openai_api_base=AZ_OAI_API_BASE_GPT_4,
openai_api_version="2023-05-15",
deployment_name=AZ_OAI_DEPLOYMENT_ID_GPT_4,
openai_api_type="azuread",
)
```
The problem is that the get_token function returns a token with a ttl of 60 minutes.
This requires us to recreate the chat object before the expiration reaches.
I think it would be great if you could add an optional function as an argument which the AzureChatOpenAI class will trigger in the retry logic incase there is a 401 API error.
Something that looks like this:
```
class AzureChatOpenAI(ChatOpenAI):
# ..... other properties
token_refresh_function: Optional[Callable[[], str]] = None
# call this on the retry function...
def _refresh_token_if_needed(self):
if self.token_refresh_function:
new_token = self.token_refresh_function()
if new_token:
self.client.api_key = new_token
```
### Motivation
The Azure models might have a more strict security requirements where the open_api key is a token with a short ttl.
### Your contribution
I would love to help just don't have the time right now, but I shared a few snippets that might help..
|
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| 1,970,446,072 |
I_kwDOIPDwls51cp74
| 12,631 |
DOC: Import Error while creating SelfQueryRetriever
|
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[] | null | 2 | 2023-10-31T13:12:27 | 2023-10-31T13:43:27 | null |
NONE
| null |
### Issue with current documentation:
Import Error while creating SelfQueryRetriever
`retriever = SelfQueryRetriever.from_llm(
llm,
vectordb,
document_content_description,
metadata_field_info,
verbose=True)`
The documentation mentions it requires lark but doesn't explicitly mention its version. I am getting an import error.
<img width="810" alt="image" src="https://github.com/langchain-ai/langchain/assets/55703712/8380cd66-5e1f-4c91-8384-7649ca802be0">
### Idea or request for content:
The documentation should mention specifically that lark version greater then 1.1.5 is required.
`pip install lark==1.1.5`
<img width="529" alt="image" src="https://github.com/langchain-ai/langchain/assets/55703712/f054f70b-672a-4938-aaf2-4fbfa16cb051">
But this also didn't solve the issue.
|
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PR_kwDOIPDwls5eN_41
| 12,630 |
Improve Runnable type inference for input_schemas
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closed
| false | null |
[] | null | 1 | 2023-10-31T11:05:24 | 2023-10-31T15:15:18 | 2023-10-31T13:22:54 |
COLLABORATOR
| null |
- Prefer lambda type annotations over inferred dict schema
- For sequences that start with RunnableAssign infer seq input type as "input type of 2nd item in sequence - output type of runnable assign"
|
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| 1,970,198,162 |
I_kwDOIPDwls51btaS
| 12,629 |
AttributeError: 'PGVector' object has no attribute 'CollectionStore'. Did you mean: 'collection_name'?
|
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open
| false | null |
[] | null | 7 | 2023-10-31T11:02:09 | 2023-11-03T13:13:16 | null |
NONE
| null |
### System Info
Langchain version. - 0.0.326
Python - 3.9.6
### Who can help?
_No response_
### 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
embeddings = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2')
CONNECTION_STRING = "postgresql+psycopg2://hwc@localhost:5432/test3"
COLLECTION_NAME = "state_of_the_union_test"
vectorestore = PGVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
)
Getting below error
line 221, in get_collection
return self.CollectionStore.get_by_name(session, self.collection_name)
^^^^^^^^^^^^^^^^^^^^
AttributeError: 'PGVector' object has no attribute 'CollectionStore'. Did you mean: 'collection_name'?
### Expected behavior
The embeddings should be stored in the database using pgvector.
|
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| 1,970,157,483 |
PR_kwDOIPDwls5eN1mv
| 12,627 |
Adding a template for Solo Performance Prompting Agent
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closed
| false | null |
[] | null | 1 | 2023-10-31T10:38:54 | 2023-11-01T15:10:08 | 2023-11-01T15:10:08 |
CONTRIBUTOR
| null |
**Description:** This template creates an agent that transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas.
**Tag maintainer:** @hwchase17
|
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| 1,970,082,248 |
I_kwDOIPDwls51bRHI
| 12,626 |
How to use FewShotPromptTemplate and RunnableBranch combination？
|
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closed
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[] | null | 3 | 2023-10-31T10:03:04 | 2023-11-01T09:19:06 | 2023-11-01T09:19:06 |
NONE
| null |
```
langchain_chain = (
FewShotPromptTemplate(
examples=examples,
example_prompt=example_prompt,
prefix=prefix,
suffix="current time {now}\n{format_instructions} \n Question: {question} ",
input_variables=["question", "now", "format_instructions"])
| ChatOpenAI()
)
branch = RunnableBranch(
(lambda x: "q1" in x["topic"].lower(), langchain_chain),
llm=ChatTongyi())),
(lambda x: "12" in x["topic"].lower(), anthropic_chain),
general_chain,
)
full_chain = {"topic": chain, "question": lambda x: x["question"], "now": now,
"format_instructions": format_instructions} | branch
print(full_chain.invoke({"question": "question"}))
````
I want to pass some parameters to the next chain after selecting the corresponding chain in RunnableBranch, here is the langchain_chain
|
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| 1,970,058,888 |
PR_kwDOIPDwls5eNfl6
| 12,625 |
LM Format Enforcer Integration + Sample Notebook
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[] | null | 2 | 2023-10-31T09:51:16 | 2023-10-31T16:49:01 | 2023-10-31T16:49:01 |
CONTRIBUTOR
| null |
## Description
This PR adds support for [lm-format-enforcer](https://github.com/noamgat/lm-format-enforcer) to LangChain.
The library is similar to jsonformer / RELLM which are supported in Langchain, but has several advantages such as
- Batching and Beam search support
- More complete JSON Schema support
- LLM has control over whitespace, improving quality
- Better runtime performance due to only calling the LLM's generate() function once per generate() call.
The integration is loosely based on the jsonformer integration in terms of project structure.
## Dependencies
No compile-time dependency was added, but if `lm-format-enforcer` is not installed, a runtime error will occur if it is trying to be used.
## Tests
Due to the integration modifying the internal parameters of the underlying huggingface transformer LLM, it is not possible to test without building a real LM, which requires internet access. So, similar to the jsonformer and RELLM integrations, the testing is via the notebook.
## Twitter Handle
[@noamgat](https://twitter.com/noamgat)
Looking forward to hearing feedback!
|
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| 1,970,027,175 |
PR_kwDOIPDwls5eNYkI
| 12,624 |
Add TileDB vectorstore implementation
|
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[] | null | 2 | 2023-10-31T09:35:49 | 2023-11-03T09:59:44 | 2023-11-03T00:21:03 |
CONTRIBUTOR
| null |
- **Description:** Add [TileDB](https://tiledb.com) vectorstore implementation. TileDB offers ANN search capabilities using the [TileDB-Vector-Search](https://github.com/TileDB-Inc/TileDB-Vector-Search) module. It provides serverless execution of ANN queries and storage of vector indexes both on local disk and cloud object stores (i.e. AWS S3). More details in:
- [Why TileDB as a Vector Database](https://tiledb.com/blog/why-tiledb-as-a-vector-database)
- [TileDB 101: Vector Search](https://tiledb.com/blog/tiledb-101-vector-search)
- **Twitter handle:** @tiledb
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PR_kwDOIPDwls5eNN8X
| 12,623 |
bump 327
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[] | null | 1 | 2023-10-31T09:12:50 | 2023-10-31T09:18:10 | 2023-10-31T09:18:09 |
COLLABORATOR
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PR_kwDOIPDwls5eM_a2
| 12,622 |
added template to use Vertex Vector Search for q&a
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CONTRIBUTOR
| null |
added template to use Vertex Vector Search for q&a
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| 1,969,872,868 |
I_kwDOIPDwls51ad_k
| 12,621 |
Issue: Creating Memory of QAChain , Making the llm not to answer from its own knowledge but rely on the context we provided
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[] | null | 1 | 2023-10-31T08:07:32 | 2023-10-31T08:28:48 | null |
NONE
| null |
### Issue you'd like to raise.
I have created a retrieval QA project. In this project, I want to add memory to LLM so that it can also remember the previous chat. I have configured the LLM not to answer from its own knowledge but from the context I provided, and it is working fine. However, when I add memory to it, it starts to answer from its own knowledge if it is out of context. I just want the LLM to answer based on the context, remember the previous chat, and if the answer is out of context, it should simply state that it is out of context or something similar. Kindly help me or suggest me something which can work.
### Suggestion:
_No response_
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I_kwDOIPDwls51Zv81
| 12,620 |
Issue: Amazon Bedrock Cohere Command - Malformed input request: 2 schema violations found, please reformat your input and try again.
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[] | null | 7 | 2023-10-31T05:43:21 | 2024-01-03T12:12:32 | 2023-11-02T04:56:45 |
NONE
| null |
### Issue you'd like to raise.
I've have been trying to work with AWS bedrock Cohere command LLM with Langchain and I'm referring [https://github.com/aws-samples/rag-using-langchain-amazon-bedrock-and-opensearch/blob/main/ask-bedrock-with-rag.py](url) as source.
**Below are few snippet of code that I'm working with,**
def create_bedrock_llm(bedrock_client):
bedrock_llm = Bedrock(
model_id="cohere.command-text-v14",
client=bedrock_client,
verbose = True,
model_kwargs={'temperature': 0}
)
return bedrock_llm
bedrock_client = bedrock_client = boto3.client(service_name="bedrock-runtime", region_name="us-east-1")
llm = create_bedrock_llm(bedrock_client)
template = """"Your are a expert financial reporter who analyzes performance of the company. The relevant information for answering the question is given below. Try to give detailed answer using the context available. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Answer:"""
prompt = PromptTemplate(template=template,
input_variables=['context', 'question'])
embedding = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
db = Chroma(persist_directory=DB_CHROMA_PATH, embedding_function=embedding)
retriever=db.as_retriever(search_kwargs={'k': 3})
qa = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={"prompt": prompt, "verbose": True},
verbose=True)
while True:
query = input("\nEnter the query\n")
if query.lower() == 'exit':
break
res = qa(query)
print(res)
**Error**:
ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: Malformed input request: 2 schema violations found, please reformat your input and try again.
### Suggestion:
I have tried with API request using invoke_model() function with the same prompt schema which worked perfectly fine and response was recieved.
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| 1,969,645,555 |
PR_kwDOIPDwls5eMFrc
| 12,619 |
Adds version CLI command
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[] | null | 1 | 2023-10-31T04:59:51 | 2023-10-31T21:50:05 | 2023-10-31T21:50:04 |
CONTRIBUTOR
| null |
Will be automatically bumped with `poetry version patch`.
@efriis @hwchase17
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| 1,969,629,110 |
PR_kwDOIPDwls5eMCIN
| 12,618 |
Adds confirmation logs after creating a new project
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CONTRIBUTOR
| null |
@efriis @hwchase17
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I_kwDOIPDwls51ZJ8d
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PGVector __post_init__ was removed in 0.0.326
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[] | null | 1 | 2023-10-31T02:40:48 | 2023-10-31T02:48:51 | null |
NONE
| null |
### System Info
Langchain: 0.0.326
__post_init__ in __init__ accidentally removed in: https://github.com/langchain-ai/langchain/commit/2c58dca5f0ca946421ef9b7cc8149d38537ee466
### Who can help?
_No response_
### 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
1. Use PGVector
2. lib/python3.11/site-packages/langchain/vectorstores/pgvector.py", line 497, in from_texts
embeddings = embedding.embed_documents(list(texts))
^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'embed_documents'
### Expected behavior
PGVector should have __post_init__ to initialize the collection
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PR_kwDOIPDwls5eLpXP
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Readme rewrite
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| 1,969,450,626 |
I_kwDOIPDwls51Y26C
| 12,614 |
Better Math Chain
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[] | null | 0 | 2023-10-31T00:59:14 | 2023-10-31T01:59:39 | 2023-10-31T01:59:39 |
NONE
| null |
### Feature request
Currently, there are two separate chains for math: LLMSymbolicMathChain and LLMMathChain. Is there a way to get the best of both words?
### Motivation
Sometimes it is better to use one over the other for my app, but currently, I can only use one.
### Your contribution
Not that I know of.
|
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PR_kwDOIPDwls5eLWzC
| 12,612 |
Add RAG example for Astra DB
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NONE
| null |
- **Description:** This PR adds a simple RAG example using Cassandra / Astra DB, powered by cassio on the backend.
- **Dependencies:** cassio>=0.1.3
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PR_kwDOIPDwls5eLVbV
| 12,611 |
bump cli to 009
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| 1,969,398,551 |
PR_kwDOIPDwls5eLQzt
| 12,610 |
run poetry build in working dir
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[] | null | 1 | 2023-10-30T23:50:18 | 2023-10-30T23:58:35 | 2023-10-30T23:58:34 |
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Was failing because was trying to build from root: https://github.com/langchain-ai/langchain/actions/runs/6700033981/job/18205251365
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PR_kwDOIPDwls5eLQWN
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add guardrails profanity
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VoyageEmbeddings
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[] | null | 1 | 2023-10-30T23:37:15 | 2023-10-31T01:37:44 | 2023-10-31T01:37:44 |
CONTRIBUTOR
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- **Description:** Integrate VoyageEmbeddings into LangChain, with tests and docs
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PR_kwDOIPDwls5eLM8l
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cli should pull instead of delete+clone
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Hybrid Search Weaviate Template
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CONTRIBUTOR
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refactor: Code Refactoring of Vertex AI Components
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CONTRIBUTOR
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- Improved Readability/Efficiency by Code Refactoring of Vertex AI Components
- Fixes #11961
- Adds error handling for aiplatform versions <1.35.0
<!-- 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_kwDOIPDwls5eLJJY
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remove poetry from cli
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PR_kwDOIPDwls5eLHXH
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Fix very small typo
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[] | null | 1 | 2023-10-30T22:59:45 | 2023-10-31T01:14:02 | 2023-10-30T23:30:18 |
CONTRIBUTOR
| null |
- **Description:** this is the world's smallest typo change of a typo I saw while reading the docs
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PR_kwDOIPDwls5eLF0r
| 12,602 |
add emailfrompdf capability
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[] | null | 4 | 2023-10-30T22:54:57 | 2023-11-03T19:35:40 | null |
NONE
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Description:
This adds a document loader to extract content and metadata from email and email-like memos which have been saved in .pdf format rather than retrieved from outlook, exchange or gmail or saved in ,eml format. metadata includes, to, cc, from, subject, date and the names of any inline-images or attachments.
This is likely to be especially useful for wading through a response to public record requests.
Issue:
email with metadata from pdfs langchain-ai/langchain#12494
Dependencies:
dateparser
pdf2image
pytesseract
Twitter handle:
tevslin
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I_kwDOIPDwls51YdQG
| 12,601 |
DataFrameLoader accepts ONLY one column and by default labeled as "text"
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NONE
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I am working with a DataFrame that has multiple columns to get their text. Why is the method only accepts one column as input?
Here is the reference to the line of the code:
https://github.com/langchain-ai/langchain/blob/481bf6fae695653c52b26895292a40cf010c08e2/libs/langchain/langchain/document_loaders/dataframe.py#L36C71-L36C71
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PR_kwDOIPDwls5eLEDE
| 12,600 |
Add support for using metadata vars to form llm prompts. Add test cas…
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[] | null | 1 | 2023-10-30T22:45:24 | 2023-11-03T11:58:59 | null |
CONTRIBUTOR
| null |
The LLM chain does allow prompt variables to be filled in by metadata variables. This commit allows for this. Also, an example for a structured chat agent that shows how metadata can be passed and used by a tool (Discussion here: https://github.com/langchain-ai/langchain/issues/12410)
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PR_kwDOIPDwls5eLBwb
| 12,599 |
Fixed missing optional tags. Added default key value for Ollama
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[] | null | 2 | 2023-10-30T22:34:50 | 2023-10-30T23:30:10 | 2023-10-30T23:30:10 |
CONTRIBUTOR
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Added missing Optional typings. Added default values for Ollama optional keys.
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PR_kwDOIPDwls5eLA_4
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Codebase RAG fireworks
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PR_kwDOIPDwls5eK8WH
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Fix BeautifulSoupTransformer: no more duplicates and correct order of tags + tests
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[] | null | 7 | 2023-10-30T22:12:10 | 2023-11-11T08:56:59 | 2023-11-11T08:56:37 |
CONTRIBUTOR
| null |
- **Description:** Fix extracting nested tags in correct order and without duplication
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @baskaryan reviewed and merged my previous similar PR
- **Twitter handle:** peter_v
Run `make format`, `make lint` and `make test` => OK
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PR_kwDOIPDwls5eK8EI
| 12,595 |
docs: removed DocCardList in `guides/` indexes
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COLLABORATOR
| null |
Removed unnecessary `DocCardList` in the `index` files. They create unnecessary duplicate of ToC in the end of pages (we also have ToC on sidebars).
this PR is continuing after #12745 12307
Problem statement:
Here is an [example](https://python.langchain.com/docs/integrations/document_transformers).
We have a sidebar with Toc and we also have a ToC at the end of the page.
The ToC at the end of the page is not necessary and it is confusing when we mix the index page styles, moreover, it requires manual work.
So, instead of the generated index.mdx pages with CardLists, I've created them manually with small text about an entity
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PR_kwDOIPDwls5eK4Lz
| 12,594 |
Remove `black` caching config from CI lint workflow.
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[] | null | 1 | 2023-10-30T21:54:47 | 2023-11-01T01:39:06 | 2023-11-01T01:39:05 |
COLLABORATOR
| null |
To merge after #12585 is merged.
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PR_kwDOIPDwls5eK4I7
| 12,593 |
factor out to_secret
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PR_kwDOIPDwls5eK1TJ
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PGVector fix
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CONTRIBUTOR
| null |
latest release broken, this fixes it
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Summarization: documentation example not replicable. Invalid response object from API together/or w/ reponse time error
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[] | null | 1 | 2023-10-30T21:29:38 | 2023-10-31T00:40:51 | null |
NONE
| null |
### System Info
Trying to replicate the basic langchain summarization type = Stuff usage as on [website documentation](https://python.langchain.com/docs/use_cases/summarization), but it is not able to process long doc.
Test model used: meta-llama/Llama-2-7b-chat-hf
Test result: tried its original demo code, it did not work, and gives two types of error alternatively after dozens of trials: **Error 1:** timeout error (I've referred to the [related langchain timeout discussion](https://github.com/langchain-ai/langchain/issues/3005) here to increase request_timeout=600/3600 secs, but still get the timeout error. checked my host is on)
**Error 2:** Invalid response object from API Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.._completion_with_retry in 4.0 seconds as it raised APIError: Invalid response object from API: '{"object":"error","message":"Expecting value: line 1 column 1 (char 0)","code":50001}' (HTTP response code was 400).
### Who can help?
Hi @hwchase17 , include this issue with you, as I am using llm **chat wrappers** like `from langchain.llms.openai import OpenAI`, `from langchain.chains.combine_documents.stuff import StuffDocumentsChain` and `from langchain.chains.summarize import load_summarize_chain` etc and error comes from there
Hi @agola11 , include you w/ this issue re **callbacks**, as in the errors mentioned above, the Error2: goes to ->
--> stuff_chainself.combine_docs(docs, callbacks=_run_manager.get_child(), **other_keys)
--> 171 return self.llm_chain.predict(callbacks=callbacks, **inputs)
--> 257 return self(kwargs, callbacks=callbacks)[self.output_key]
--> 310 raise e
--> 304 self._call(inputs, run_manager=run_manager)
---> 93 response = self.generate([inputs], run_manager=run_manager)
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
To get something out from langchain summarization, I downsized the model from llama2-7b to "Sheared-LLaMA-1.3B" and replaced the original example's doc with a shorter doc "". I can see from langchain w/ verbose started w/ > Entering new StuffDocumentsChain chain..., > Entering new LLMChain chain... w/ print out the original full doc, but it returns'> Finished chain. returns "Got any creative ideas for a 10 year old’s birthday?"'
```
from langchain.chat_models import ChatOpenAI
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
prompt_template = """Write a concise summary of the following:
"{text}"
CONCISE SUMMARY:"""
prompt = PromptTemplate.from_template(prompt_template)
llm = ChatOpenAI(verbose = True
, temperature=0.1
, model_name=model_name
, openai_api_key = "EMPTY"
, openai_api_base = "http://localhost:8000/v1"
, request_timeout=600
, max_retries=1)
llm_chain = LLMChain(llm=llm, prompt=prompt, verbose = True)
stuff_chain = StuffDocumentsChain(
llm_chain=llm_chain, document_variable_name="text"
)
loader = WebBaseLoader("https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B")
docs = loader.load()
print(stuff_chain.run(docs ))
```
But I got strange result as below:
<img width="692" alt="image" src="https://github.com/langchain-ai/langchain/assets/66184093/ef38f029-9d0c-401e-8b66-7312e44d75b8">
(my local host heart beats well)
Try the same code with a small piece of business-related conversation doc(~ 1k words) w/ StuffDocumentsChain returns something relevant, but load_summarize_chain only gives
<img width="713" alt="image" src="https://github.com/langchain-ai/langchain/assets/66184093/0d776be5-1288-49c7-8f6f-ec41171ce2c1">
More problem comes, when I double the size on my test doc(4k words, definitely have to be>4096 tokens, that's the point of using summarization), it returns Error 1 and Error 2 mentioned at the beginning, same as attempting replication langchain documentation, so get the trouble it is not able to process long doc gain.
### Expected behavior
returns some relavent result. though understand it does not have to be good, as I am using toy model 1.3Brelevant
|
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Conversational Feedback
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[] | null | 1 | 2023-10-30T21:25:00 | 2023-10-31T15:34:20 | 2023-10-31T15:34:19 |
COLLABORATOR
| null |
Context in the README.
Show how score chat responses based on a followup from the user and then log that as feedback in LangSmith
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hosting note
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Add RAG template for Timescale Vector
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[] | null | 3 | 2023-10-30T20:39:24 | 2023-10-31T17:00:05 | 2023-10-31T16:59:44 |
CONTRIBUTOR
| null |
**Description:** Add RAG template for Timescale Vector
Also fix UTM links to ensure 90-day trial.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
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|
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Feat auto hftextgen
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[] | null | 2 | 2023-10-30T20:33:39 | 2023-10-30T20:38:41 | null |
CONTRIBUTOR
| null |
**Description**
I have developed a class `HuggingFaceTextGenInferenceAuto` to automate the process of managing Docker inference server containers for the `HuggingFaceTextGenerationInference` LLM. It essentially acts as a factory to create an instance of `HuggingFaceTextGenInference` via the `from_docker` classmethod. This class can create, reuse, or recreate containers as-needed based on parameter changes.
The objective is to simplify the management of HuggingFace TextGen inference servers, and allow users to spin up Docker containers for model inference with ease. The code also contains methods to handle the health checks, wait for model downloads, and ensure container and API readiness before proceeding.
**Issue**
See feature request issue #12564
**Dependencies**
Updated toml and lockfile per contrib guidelines.
```
docker = "^6.1.3"
pytest-skip-slow = "^0.0.5"
text-generation = "^0.6.1"
```
**Twitter Handle**: [@daniel_c_barker](https://twitter.com/daniel_c_barker)
<!-- 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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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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