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Made some Grammatical error fixes
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CONTRIBUTOR
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Made some Grammatical error fixes.
CC: @baskaryan, @eyurtsev, @rlancemartin.
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fix syntax
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PR_kwDOIPDwls5Zbj1h
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Harrison/hf lru
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Harrison/string inplace
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PR_kwDOIPDwls5ZbeGW
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fake messages list model
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COLLABORATOR
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create a fake chat model that you can configure with list of messages
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PR_kwDOIPDwls5ZbZdd
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Add FalkorDB to imports
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I_kwDOIPDwls5wAUbH
| 10,150 |
why not found ernie bot from baidu in llms package? Thanks
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[] | null | 2 | 2023-09-03T15:08:29 | 2023-09-04T02:48:52 | 2023-09-04T02:48:52 |
NONE
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### Issue you'd like to raise.
_No response_
### Suggestion:
_No response_
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Refactor text splitters split and merge
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[] | null | 5 | 2023-09-03T14:28:42 | 2023-11-27T17:50:47 | null |
CONTRIBUTOR
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#9843
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- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
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2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5ZbHGU
| 10,148 |
Uncomment create_vector_extension in __post_init__ of PGVector class
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[] | null | 2 | 2023-09-03T13:43:16 | 2023-09-03T20:35:17 | 2023-09-03T19:55:13 |
NONE
| null |
I have the 0-day experience with PGVector in mind. I can't use PGVector.create_vector_extension at all if the `vector` extension is not already created on postgres. If one initializes PGVector without the extension, it fails since __post_init__ tries to create tables and that fails without the extension.
Is there a reason why create_vector_extension is commented from __post_init__? If not then uncommenting it should fix this issue.
#9511
twitter @skydowx
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PR_kwDOIPDwls5ZbDaB
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added llama-chat-content formatter support
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[] | null | 1 | 2023-09-03T12:53:22 | 2023-09-03T20:00:04 | 2023-09-03T20:00:04 |
CONTRIBUTOR
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<!-- Thank you for contributing to LangChain!
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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!
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https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
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PR_kwDOIPDwls5Za6ZO
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Update apis.ipynb
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[] | null | 1 | 2023-09-03T10:14:53 | 2023-09-03T20:00:22 | 2023-09-03T20:00:22 |
CONTRIBUTOR
| null |
few typo fixes
|
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PR_kwDOIPDwls5Za6R0
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Update openai_multi_functions_agent.ipynb
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[] | null | 1 | 2023-09-03T10:12:36 | 2023-09-03T20:00:48 | 2023-09-03T20:00:48 |
CONTRIBUTOR
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typo fix
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I_kwDOIPDwls5v_7V8
| 10,143 |
Issue: DRF response streaming
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NONE
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How can I stream the OpenAI response in DRF?
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PR_kwDOIPDwls5Zaody
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Update index.mdx
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[] | null | 2 | 2023-09-03T05:21:24 | 2023-09-03T05:36:27 | 2023-09-03T05:36:27 |
CONTRIBUTOR
| null |
fixed typos
|
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PR_kwDOIPDwls5Zaoa8
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Update chat_vector_db.mdx
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[] | null | 2 | 2023-09-03T05:19:54 | 2023-09-03T05:36:10 | 2023-09-03T05:36:10 |
CONTRIBUTOR
| null |
typo fix
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PR_kwDOIPDwls5Zamyj
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Implemented MMR search for Redis
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[] | null | 8 | 2023-09-03T04:28:06 | 2023-09-08T22:14:45 | 2023-09-08T22:14:44 |
CONTRIBUTOR
| null |
Description: Implemented MMR search for Redis. Pretty straightforward, just using the already implemented MMR method on similarity search–fetched docs.
Issue: #10059
Dependencies: None
Twitter handle: @hamza_tahboub
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PR_kwDOIPDwls5Zadrz
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feat(llms): add model_kwargs to hf tgi
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[] | null | 2 | 2023-09-02T23:54:18 | 2023-09-04T07:24:14 | 2023-09-04T07:24:14 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
@baskaryan
Following what we discussed in #9724 and your suggestion, I've added a `model_kwargs` parameter to hf tgi.
|
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PR_kwDOIPDwls5Zac45
| 10,138 |
make hub push public by default
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[] | null | 4 | 2023-09-02T23:14:01 | 2023-12-04T17:06:58 | 2023-12-04T17:06:50 |
COLLABORATOR
| null |
### Feature request
[Graph Of Thoughts](https://arxiv.org/pdf/2308.09687.pdf) looks promising.
It is possible to implement it with LangChain?
### Motivation
More performant prompt technique
### Your contribution
I can help with documentation.
|
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PR_kwDOIPDwls5Zabbs
| 10,136 |
Add parser and loader for Azure document intelligence service.
|
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[] | null | 4 | 2023-09-02T22:48:33 | 2023-09-09T20:23:10 | 2023-09-03T21:25:39 |
CONTRIBUTOR
| null |
Hi,
this PR contains loader / parser for Azure Document intelligence which is a ML-based service to ingest arbitrary PDFs / images, even if scanned. The loader generates Documents by pages of the original document. This is my first contribution to LangChain.
Unfortunately I could not find the correct place for test cases. Happy to add one if you can point me to the location, but as this is a cloud-based service, a test would require network access and credentials - so might be of limited help.
Dependencies: The needed dependency was already part of pyproject.toml, no change.
Twitter: feel free to mention @LarsAC on the announcement
|
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Connection Timeout and Max Retries Exceeded in HTTPS Request
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[] | null | 1 | 2023-09-02T22:18:19 | 2023-09-03T19:47:46 | 2023-09-03T19:47:46 |
NONE
| null |
### System Info
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/util/connection.py", line 95, in create_connection
raise err
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
TimeoutError: [Errno 60] Operation timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/connection.py", line 179, in _new_conn
raise ConnectTimeoutError(
urllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x159e351d0>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/connectionpool.py", line 787, in urlopen
retries = retries.increment(
^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/urllib3/util/retry.py", line 592, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x159e351d0>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/fsndzomga/Downloads/react-mega/LCEL2.py", line 29, in <module>
vectorstore = Chroma.from_texts([obama_text], embedding=OpenAIEmbeddings())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/vectorstores/chroma.py", line 576, in from_texts
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/vectorstores/chroma.py", line 186, in add_texts
embeddings = self._embedding_function.embed_documents(texts)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/embeddings/openai.py", line 478, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/embeddings/openai.py", line 331, in _get_len_safe_embeddings
encoding = tiktoken.encoding_for_model(model_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tiktoken/model.py", line 75, in encoding_for_model
return get_encoding(encoding_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tiktoken/registry.py", line 63, in get_encoding
enc = Encoding(**constructor())
^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tiktoken_ext/openai_public.py", line 64, in cl100k_base
mergeable_ranks = load_tiktoken_bpe(
^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tiktoken/load.py", line 114, in load_tiktoken_bpe
contents = read_file_cached(tiktoken_bpe_file)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tiktoken/load.py", line 46, in read_file_cached
contents = read_file(blobpath)
^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tiktoken/load.py", line 24, in read_file
return requests.get(blobpath).content
^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/requests/api.py", line 73, in get
return request("get", url, params=params, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/requests/adapters.py", line 553, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x159e351d0>, 'Connection to openaipublic.blob.core.windows.net timed out. (connect timeout=None)'))
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here is my code:
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from operator import itemgetter
from apikey import OPENAI_API_KEY
import os
# Set the OpenAI API key
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
# Initialize the ChatOpenAI model
model = ChatOpenAI()
# Create a long text about Barack Obama to serve as the context
obama_text = """
Barack Obama served as the 44th President of the United States from 2009 to 2017.
He was born in Honolulu, Hawaii, on August 4, 1961. Obama is a graduate of Columbia University
and Harvard Law School, where he served as president of the Harvard Law Review. He was a community
organizer in Chicago before earning his law degree and worked as a civil rights attorney and taught
constitutional law at the University of Chicago Law School between 1992 and 2004. He served three
terms representing the 13th District in the Illinois Senate from 1997 until 2004, when he ran for the
U.S. Senate. Obama received the Nobel Peace Prize in 2009.
"""
# Create the retriever with the Obama text as the context
vectorstore = Chroma.from_texts([obama_text], embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
# Define the prompt template
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
# Create the chain for answering questions
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
# Invoke the chain to answer a question
print(chain.invoke("When was Barack Obama born?"))
# Create a new prompt template that allows for translation
template_with_language = """Answer the question based only on the following context:
{context}
Question: {question}
Answer in the following language: {language}
"""
prompt_with_language = ChatPromptTemplate.from_template(template_with_language)
# Create the chain for answering questions in different languages
chain_with_language = {
"context": itemgetter("question") | retriever,
"question": itemgetter("question"),
"language": itemgetter("language")
} | prompt_with_language | model | StrOutputParser()
# Invoke the chain to answer a question in Italian
print(chain_with_language.invoke({"question": "When was Barack Obama born?", "language": "italian"}))
### Expected behavior
a response to my question
|
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PR_kwDOIPDwls5ZaXuy
| 10,134 |
add additional model support to chatlitellm
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[] | null | 1 | 2023-09-02T21:20:48 | 2023-09-04T07:16:40 | 2023-09-04T07:16:40 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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[] | null | 1 | 2023-09-02T19:23:47 | 2023-09-04T07:21:34 | 2023-09-04T07:21:34 |
COLLABORATOR
| null |
Fix for: https://github.com/langchain-ai/langchain/issues/10019
Verified fix manually
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| 1,878,784,909 |
PR_kwDOIPDwls5ZaLaS
| 10,132 |
enable serde retrieval qa with sources
|
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[] | null | 2 | 2023-09-02T17:48:36 | 2023-09-08T23:57:10 | 2023-09-08T23:57:10 |
CONTRIBUTOR
| null |
#3983 mentions serialization/deserialization issues with both `RetrievalQA` & `RetrievalQAWithSourcesChain`.
`RetrievalQA` has already been fixed in #5818.
Mimicing #5818, I added the logic for `RetrievalQAWithSourcesChain`.
|
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PR_kwDOIPDwls5ZaKYg
| 10,131 |
adding suffixes
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[] | null | 1 | 2023-09-02T17:30:47 | 2023-09-03T20:22:04 | 2023-09-03T20:15:20 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
**Description**: Adding support for specifying the suffix to append to HumanMessage, AIMessage and SystemMessage. Certain models, such as Llama2-*-chat, expects a certain way of formatting the prompt, which includes encasing the message in a certain construct (`[INST] ... [/INST]` for HumanMessage and `<<SYS>>...<</SYS>>` for SystemMessage which cannot __easily__ be achieved in the current state of langchain.
At this point in time I haven't added support for also specifying Beginning-of-String (BOS) and End-of-String (EOS) tokens, as this will likely have to be made at the prompt level and not the message level.
**Issue**: Not aware of any existing ones and instead of creating one I jumped directly into creating a PR. Let me know if me creating an issue is desirable.
**Dependencies**: None
**Tag maintainer**:
**Twitter handle**:
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| 1,878,776,365 |
PR_kwDOIPDwls5ZaJuv
| 10,130 |
bump 13
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[] | null | 1 | 2023-09-02T17:22:21 | 2023-09-02T17:22:32 | 2023-09-02T17:22:31 |
COLLABORATOR
| null | null |
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| 1,878,736,357 |
I_kwDOIPDwls5v-z3l
| 10,128 |
ConversationalRetrievalChain doesn't work with Azure OpenAI
|
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[] | null | 3 | 2023-09-02T15:34:17 | 2023-12-10T16:04:45 | 2023-12-10T16:04:44 |
NONE
| null |
### System Info
Latest
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use ConversationalRetrievalChain with an Azure OpenAI gpt model
This is the code:
llm = AzureOpenAI(engine=OPENAI_DEPLOYMENT_NAME, model=OPENAI_MODEL, temperature=0.0)
qa = ConversationalRetrievalChain.from_llm(llm, retriever)
I get the following error:
usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for LLMChain
llm
Can't instantiate abstract class BaseLanguageModel with abstract methods agenerate_prompt, apredict, apredict_messages, generate_prompt, invoke, predict, predict_messages (type=type_error)
### Expected behavior
The code executes without any error
|
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| 1,878,707,467 |
I_kwDOIPDwls5v-s0L
| 10,126 |
ModuleNotFoundError: No module named 'langchain_experimental.data_anonymizer'
|
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[] | null | 6 | 2023-09-02T14:16:42 | 2024-01-15T11:55:37 | 2023-09-03T02:32:01 |
NONE
| null |
### System Info
Langchain 0.0.279 / Langchain experimental 0.0.12/ Python 3.10
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Follow the guideline: https://python.langchain.com/docs/guides/privacy/presidio_data_anonymization
I was able to make it work by downloading the source of the langchain experimental and copy & pasting the missing folder into the local lib folder.
However, this won't be a good option when building a docker image. Why isn't it part of the experimental library 0.0.12?
### Expected behavior
Example codes will work.
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I_kwDOIPDwls5v-pVp
| 10,125 |
SageMaker Endpoints with Streaming Capability
|
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| false | null |
[] | null | 2 | 2023-09-02T13:37:32 | 2023-10-25T15:25:22 | null |
CONTRIBUTOR
| null |
### Feature request
With the new announced [support for Streaming in SageMaker Endpoints](https://aws.amazon.com/blogs/machine-learning/elevating-the-generative-ai-experience-introducing-streaming-support-in-amazon-sagemaker-hosting/), Langchain can add a Streaming capability to the `SagemakerEndpoint` class.
We can leverage the code that already exists as part of the blog post and extend where required by using the [boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker-runtime/client/invoke_endpoint_with_response_stream.html) API
### Motivation
Decreasing latency is important for developers. Streaming is currently available in `OpenAI`, `ChatOpenAI`, `ChatAnthropic`, `Hugging Face Text Generation Inference`, and `Replicate`. Having `SageMaker` opens the possibilities to a wide range of developers that leverage this AWS Service.
### Your contribution
I can work on this feature to start the process with guidance from the core development team and the community.
|
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| 1,878,669,487 |
PR_kwDOIPDwls5ZZ1JE
| 10,124 |
Added embeddings support for ollama
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[] | null | 9 | 2023-09-02T12:31:53 | 2023-09-15T00:49:32 | 2023-09-15T00:42:39 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: Added support for Ollama embeddings
- Issue: the issue # it fixes (if applicable),
- Dependencies: N/A
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @herrjemand
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
cc https://github.com/jmorganca/ollama/issues/436
|
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| 1,878,495,416 |
I_kwDOIPDwls5v95C4
| 10,122 |
Proposal for 'ConverseUntilAnswered' Agent
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[] | null | 2 | 2023-09-02T08:56:43 | 2023-12-09T16:04:16 | 2023-12-09T16:04:16 |
NONE
| null |
### Feature request
**Issue Description:**
I would like to propose the addition of a new Agent called something like 'ConverseUntilAnswered.' This new agent would engage in multi-step interactions with users to obtain an answer according to a required schema, and self-terminate when an answer is obtained.
**Example Use Case:**
Let's consider a scenario where a chatbot needs to collect user satisfaction ratings for its website. The 'ConverseUntilAnswered' agent could be used to facilitate this interaction. Perhaps this is achieved by the LangChain class injecting System messages to ChatGPT at specific times. Consider in the following dialogue that all System messages are injected by the LangChain class.
1. **System Message:** Your task is to ask the user how satisfied they are with this website on a scale from 1 to 10, where 10 is the highest. You are limited to a maximum of 2 interactions with the user. If you are reasonably certain of the user’s score, you are permitted to guess their answer. If you are unable to determine a numerical score, give a rating of “NA”. When you are ready to give a score, reply in the following format: ```ANSWER:<integer score>``` for example, for an answer of 5, you would reply ```ANSWER:5``` and add nothing else to your response.
2. **Chatbot:** On a scale from 1 to 10, how satisfied are you with this website?
3. **User:** Oh, it’s OK.
4. **Chatbot:** It sounds like you are moderately satisfied. Would you say your satisfaction is a 5 out of 10?
5. **User:** Maybe, it depends on the day.
6. **System Message:** You have used 2 of your 3 available interactions with the user. Please no longer interact with the user, but simply state your final response with the format previously specified.
8. **Chatbot:** ```ANSWER:NA```
By introducing a 'ConverseUntilAnswered' agent, we make it possible to develop an application that conducts a lengthy interview over a series of questions as defined by the programmer. For example:
1. Ask the customer their satisfaction with the website (1-10 scale)
2. Ask whether the customer is likely to recommend this website to their friends (Why or Why not)
3. Ask what feature they would like to see added to this website.
### Motivation
**Justification:**
There is a need for specialized functionality for chatbots that can conduct multi-step conversations and self-terminate once a satisfactory response is obtained. Currently the only way I know to do this is to equip an Agent with a tool that it calls when a task has been completed, but there is no guarantee the agent will ever call that tool, leaving the chatbot open to a lengthy never-ending conversation with the user. By introducing a specialized 'ConverseUntilAnswered' agent, we can minimize calls to the LLM API and ensure closure of a line of inquery to the user.
### Your contribution
I have only once before contributed to an OpenSource project, and this would be my first contribution to the LangChain project. I humbly ask for the community's guidance regarding possible best approaches for implementation using existing LangChain features, and what else should be considered before making a Pull Request.
|
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PR_kwDOIPDwls5ZY3Pm
| 10,121 |
Updated Neptune graph to use boto
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[] | null | 5 | 2023-09-02T05:23:32 | 2023-09-20T23:13:28 | 2023-09-19T23:03:08 |
CONTRIBUTOR
| null |
## Description
This PR updates the `NeptuneGraph` class to start using the boto API for connecting to the Neptune service. With boto integration, the graph class now supports authenticating requests using Sigv4; this is encapsulated with the boto API, and users only have to ensure they have the correct AWS credentials setup in their workspace to work with the graph class.
This PR also introduces a conditional prompt that uses a simpler prompt when using the `Anthropic` model provider. A simpler prompt have seemed to work better for generating cypher queries in our testing.
**Note**: This version will require boto3 version 1.28.38 or greater to work.
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I_kwDOIPDwls5v9GE0
| 10,120 |
DOC: Inexistent API documentation about SQLRecordManager
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[] | null | 2 | 2023-09-02T02:38:30 | 2023-12-30T16:06:39 | 2023-12-30T16:06:38 |
NONE
| null |
### Issue with current documentation:
There is no documentation about the `SQLRecordManager` in the [langchain.indexes.base.RecordManager](https://api.python.langchain.com/en/latest/indexes/langchain.indexes.base.RecordManager.html#langchain.indexes.base.RecordManager) documentation, although is used in an indexing example [here](https://python.langchain.com/docs/modules/data_connection/indexing#quickstart).
### Idea or request for content:
Please include the documentation for `SQLRecordManager`, as well as other supported databases that may be currently available to use as a Record Manager.
|
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PR_kwDOIPDwls5ZYnWs
| 10,119 |
Support `ensure_trailing_slash` and `headers` options in RecursiveUrlLoader
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CONTRIBUTOR
| null |
This commit adds the following support:
- `ensure_trailing_slash` ... Ensure a trailing slash when requesting to the given url or a child link.
- This option is necessary when the site doesn't support trailing slash e.g. `https://wiki.supercombo.gg/w/Street_Fighter_6` works but `https://wiki.supercombo.gg/w/Street_Fighter_6/` doesn't work.
- `headers` ... Headers when requesting to the url.
- This option is necessary when the site requires specific header to respond e.g. `https://www.streetfighter.com/6/character` requires `user-agent` header.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,878,277,419 |
I_kwDOIPDwls5v9D0r
| 10,118 |
Indexing deletion modes not working
|
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[] | null | 3 | 2023-09-02T02:31:04 | 2023-12-09T16:04:21 | 2023-12-09T16:04:21 |
NONE
| null |
### System Info
LangChain version: 0.0.276
Python version: 3.11.2
Platform: x86_64 Debian 12.2.0-14
Weaviate as vectorstore
SQLite for document index
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Followed the provided [Indexing directions](https://python.langchain.com/docs/modules/data_connection/indexing) of LangChain's documentation.
```
import os, time, json, weaviate, openai
from langchain.vectorstores import Weaviate
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.indexes import SQLRecordManager, index
from langchain.text_splitter import CharacterTextSplitter
from langchain.schema import Document
from datetime import datetime
VECTORS_INDEX_NAME = 'LaborIA_vectors_TEST'
COLLECTION_NAME = 'LaborIA_docs_TEST'
NAMESPACE = f"weaviate/{COLLECTION_NAME}"
record_manager = SQLRecordManager(NAMESPACE, db_url="sqlite:///record_manager_cache.sql")
record_manager.create_schema()
def _clear():
"""Hacky helper method to clear content. See the `full` mode section to to understand why it works."""
index([], record_manager, weaviate_vectorstore, cleanup="full", source_id_key="source")
_clear()
```
Results in the following error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[60], line 1
----> 1 _clear()
Cell In[59], line 3, in _clear()
1 def _clear():
2 """Hacky helper method to clear content. See the `full` mode section to to understand why it works."""
----> 3 index([], record_manager, weaviate_vectorstore, cleanup="full", source_id_key="source")
TypeError: index() got an unexpected keyword argument 'cleanup'
```
Declaring the `_clear()` function with either `cleanup="incremental"` or `cleanup=None` deletion modes results in the same `TypeError`. It shows no errors if the `cleanup` parameter is completely removed.
Will `index` execute in deletion mode `None` (as specified [here](https://python.langchain.com/docs/modules/data_connection/indexing#none-deletion-mode) if the `cleanup` parameter is not present?
### Expected behavior
No errors when the `cleanup` mode parameter is set when `index` is called.
|
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| 1,878,208,474 |
PR_kwDOIPDwls5ZYYn4
| 10,117 |
bump 280
|
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[] | null | 1 | 2023-09-02T00:35:38 | 2023-09-02T00:43:15 | 2023-09-02T00:43:14 |
COLLABORATOR
| null | null |
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| 1,878,207,224 |
PR_kwDOIPDwls5ZYYXQ
| 10,116 |
Allow returning message objects for fake chat LLM
|
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[] | null | 2 | 2023-09-02T00:34:07 | 2023-09-04T22:09:15 | 2023-09-03T20:33:47 |
CONTRIBUTOR
| null |
Checks if a returned response is already a message object.
If so, returns it directly, if not, build one.
Allows the fake chat LLM to return more robust fake responses, e.g. function calls
P.S. I'm not entirely sure this is the correct solution, if anyone has a better idea, lemme know.
|
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| 1,878,194,788 |
PR_kwDOIPDwls5ZYVtL
| 10,115 |
rm sqlite3 import
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[] | null | 1 | 2023-09-02T00:12:50 | 2023-09-02T00:14:07 | 2023-09-02T00:14:07 |
COLLABORATOR
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PR_kwDOIPDwls5ZYLud
| 10,114 |
Fix url loader
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[] | null | 3 | 2023-09-01T23:13:05 | 2023-09-25T15:45:53 | 2023-09-25T15:45:53 |
COLLABORATOR
| null |
2 things (the python reference docs highlight this)
- We inappropriately add a '/' on files with a .html suffix
- We are over-aggressive for these cases as well (match against the full path vs. the directory it's in)
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| 1,878,148,061 |
PR_kwDOIPDwls5ZYLXj
| 10,113 |
Fixing regex to support all valid JSON formats
|
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[] | null | 3 | 2023-09-01T23:11:57 | 2023-09-03T20:48:46 | 2023-09-03T20:43:32 |
NONE
| null |
**Description:**
The regex used to match JSONs was only to match curly braces, regex now has changed to support square braces
e.g `'["This is valid!"]'`
added test to verify list is also supported as valid JSON
closes [#10057](https://github.com/langchain-ai/langchain/issues/10057)
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| 1,878,116,654 |
I_kwDOIPDwls5v8cku
| 10,112 |
Cannot have models with BaseLanguageModel in pydantic 2: TypeError: BaseModel.validate() takes 2 positional arguments but 3 were given
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[] | null | 3 | 2023-09-01T22:33:12 | 2023-11-16T14:53:22 | 2023-09-06T04:18:49 |
CONTRIBUTOR
| null |
### System Info
```sh
pydantic==2.3.0
langchain==0.0.279
```
### Who can help?
@agola11 , @hwchase17: Having trouble learning how to have LLMs as attributes in pydantic2. I keep getting this confusing error which I cannot troubleshoot.
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```py
import os
os.environ['OPENAI_API_KEY'] = 'foo'
from pydantic import BaseModel
from langchain.base_language import BaseLanguageModel
from langchain.llms import OpenAI
class Foo(BaseModel):
llm: BaseLanguageModel = OpenAI()
llm = OpenAI()
# Works
Foo()
# Fails
Foo(llm=llm)
```
Error:
```sh
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[1], line 14
12 Foo()
13 # Fails
---> 14 Foo(llm=llm)
File ~/miniconda3/envs/chain/lib/python3.11/site-packages/pydantic/main.py:165, in BaseModel.__init__(__pydantic_self__, **data)
163 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
164 __tracebackhide__ = True
--> 165 __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
TypeError: BaseModel.validate() takes 2 positional arguments but 3 were given
```
### Expected behavior
Be able to set the field without validation errors
|
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I_kwDOIPDwls5v8XqX
| 10,111 |
Got ValidationError when searching a content does not exist in the Pinecone vector store using Langchain Pinecone connection
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[] | null | 3 | 2023-09-01T22:05:26 | 2023-12-18T23:48:02 | 2023-12-18T23:48:02 |
NONE
| null |
### System Info
```
File Python\Python39\site-packages\langchain\vectorstores\pinecone.py:301, in Pinecone.max_marginal_relevance_search(self, query, k, fetch_k, lambda_mult, filter,
namespace, **kwargs)
284 """Return docs selected using the maximal marginal relevance.
285
286 Maximal marginal relevance optimizes for similarity to query AND diversity
(...)
298 List of Documents selected by maximal marginal relevance.
299 """
300 embedding = self._embed_query(query)
--> 301 return self.max_marginal_relevance_search_by_vector(
302 embedding, k, fetch_k, lambda_mult, filter, namespace
303 )
File Python\Python39\site-packages\langchain\vectorstores\pinecone.py:269, in Pinecone.max_marginal_relevance_search_by_vector(self, embedding, k, fetch_k, lambda_mult, filter, namespace, **kwargs)
262 mmr_selected = maximal_marginal_relevance(
263 np.array([embedding], dtype=np.float32),
264 [item["values"] for item in results["matches"]],
265 k=k,
266 lambda_mult=lambda_mult,
267 )
268 selected = [results["matches"][i]["metadata"] for i in mmr_selected]
--> 269 return [
270 Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
271 for metadata in selected
272 ]
File Python\Python39\site-packages\langchain\vectorstores\pinecone.py:270, in <listcomp>(.0)
262 mmr_selected = maximal_marginal_relevance(
263 np.array([embedding], dtype=np.float32),
264 [item["values"] for item in results["matches"]],
265 k=k,
266 lambda_mult=lambda_mult,
267 )
268 selected = [results["matches"][i]["metadata"] for i in mmr_selected]
269 return [
--> 270 Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
271 for metadata in selected
272 ]
File Python\Python39\site-packages\langchain\load\serializable.py:74, in Serializable.__init__(self, **kwargs)
73 def __init__(self, **kwargs: Any) -> None:
---> 74 super().__init__(**kwargs)
75 self._lc_kwargs = kwargs
File Python\Python39\site-packages\pydantic\v1\main.py:341, in BaseModel.__init__(__pydantic_self__, **data)
339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
--> 341 raise validation_error
342 try:
343 object_setattr(__pydantic_self__, '__dict__', values)
ValidationError: 1 validation error for Document
page_content
str type expected (type=type_error.str)
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Connect to a Pinecone vector store
```
vectorstore = Pinecone(pinecone_index, embedding_model, "text")
```
2. Use MMR search to query the vector store. Here, my vectorstore **does not have any information about Bill Gates**.
```
query = "Who is Bill Gates?"
res = vectorstore.max_marginal_relevance_search(
query=query,
k=4,
fetch_k=20,
lambda_mult=0.5
)
```
3. Got the error
By the way, here is the [API doc](https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html#langchain.vectorstores.pinecone.Pinecone.max_marginal_relevance_search) for the `max_marginal_relevance_search`.
### Expected behavior
It should show it cannot find any context instead of raising an error.
|
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PR_kwDOIPDwls5ZX_3f
| 10,110 |
rm base64 images from docs
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[] | null | 1 | 2023-09-01T22:03:43 | 2023-09-01T22:23:31 | 2023-09-01T22:15:12 |
COLLABORATOR
| null |
don't render after markdown conversion anyways
|
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PR_kwDOIPDwls5ZX_zg
| 10,109 |
Update redirects meta tags
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Bug-fix CSV Agent - Parsing Tool Inputs
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[] | null | 5 | 2023-09-01T20:15:39 | 2023-11-29T01:57:29 | 2023-11-29T01:57:28 |
NONE
| null |
Issue & Description:
Suggested changes are due to encountering constant constant errors when using the Pandas DataFrame Agent due to parsing the python code that comes in the responses from the LLMs.
To address the issue I propose json serialization of the arguments of the function call at the OpenAIFunctionsAgent, as well as a more detailed ArgSchema for the PythonAstREPLTool, and finally an addition to the input query sanitize function, since i've noticed that more often than not, OpenAI's GPT-3.5-turbo make function calls but instead of sending the "query" param as a plan python code string, it tries to serialize it into some sort of "{'query': 'python code snippet'}" string, which is just not what we want.
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| 1,877,926,816 |
PR_kwDOIPDwls5ZXb0y
| 10,107 |
Bagatur/fix multiquery
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[] | null | 1 | 2023-09-01T19:05:22 | 2023-09-04T07:33:25 | 2023-09-04T07:33:25 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,877,924,878 |
I_kwDOIPDwls5v7twO
| 10,106 |
Using SQLDatabase with Llama 2 for snowflake connection, i get ProgramingError
|
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[] | null | 2 | 2023-09-01T19:03:24 | 2023-12-08T16:04:15 | 2023-12-08T16:04:14 |
NONE
| null |
### System Info
Langchain 0.0.277
Python 3.9
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents import create_sql_agent, initialize_agent, create_spark_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit, SparkSQLToolkit
from langchain.sql_database import SQLDatabase
from langchain_experimental.sql.base import SQLDatabaseChain
from langchain.llms import HuggingFacePipeline
from langchain.agents import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.chat_models import ChatOpenAI
from snowflake.sqlalchemy import URL
from sqlalchemy import create_engine
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model_id= "Photolens/llama-2-13b-langchain-chat"
user= "***"
password= "***"
account= "**-**"
database="**SNOWFLAKE_SAMPLE_DATA*"
schema="****"
warehouse="***"
def load_model():
model_id = "Photolens/llama-2-13b-langchain-chat"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map='auto',
low_cpu_mem_usage=True,
trust_remote_code=True
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_length=1100,
repetition_penalty=1.15,
top_p=0.95,
temperature=0.2,
pad_token_id=tokenizer.eos_token_id,
max_new_tokens=300
)
local_llm = HuggingFacePipeline(pipeline=pipe)
return local_llm
LLM=load_model()
engine = create_engine(URL(
user= "***",
password= "****",
account= "**-**",
database="**SNOWFLAKE_SAMPLE_DATA,
schema="***",
warehouse="***")
)
db = SQLDatabase(engine)
#here comes the problem, SQLDatabase makes a wrong query on Snowflake
### Expected behavior
I'm expecting to generate a connection with SQLDatabase, but it doesn't in fact makes a wrong query that i dont get it. I'm new at this so i would apreciate some help.
This is the error a i get:
/home/zeusone/anaconda3/envs/snowflake_ai/bin/python /home/zeusone/Documents/ChatbotFalcon/SQLagent/snoflake_simple_agent.py
/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/options.py:96: UserWarning: You have an incompatible version of 'pyarrow' installed (13.0.0), please install a version that adheres to: 'pyarrow<8.1.0,>=8.0.0; extra == "pandas"'
warn_incompatible_dep(
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:15<00:00, 5.20s/it]
Traceback (most recent call last):
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1910, in _execute_context
self.dialect.do_execute(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 736, in do_execute
cursor.execute(statement, parameters)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/cursor.py", line 827, in execute
Error.errorhandler_wrapper(self.connection, self, error_class, errvalue)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/errors.py", line 275, in errorhandler_wrapper
handed_over = Error.hand_to_other_handler(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/errors.py", line 330, in hand_to_other_handler
cursor.errorhandler(connection, cursor, error_class, error_value)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/errors.py", line 209, in default_errorhandler
raise error_class(
snowflake.connector.errors.ProgrammingError: 001059 (22023): SQL compilation error:
Must specify the full search path starting from database for SNOWFLAKE_SAMPLE_DATA
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/zeusone/Documents/ChatbotFalcon/SQLagent/snoflake_simple_agent.py", line 56, in <module>
db = SQLDatabase(engine)#"snowflake://ADRIANOCABRERA:Semilla_1@EKKFOPI-YK08475/SNOWFLAKE_SAMPLE_DATA/TPCH-SF1?warehouse=COMPUTE_WH")
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/langchain/utilities/sql_database.py", line 111, in __init__
self._metadata.reflect(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/sql/schema.py", line 4901, in reflect
Table(name, self, **reflect_opts)
File "<string>", line 2, in __new__
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/util/deprecations.py", line 375, in warned
return fn(*args, **kwargs)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/sql/schema.py", line 619, in __new__
metadata._remove_table(name, schema)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__
compat.raise_(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 211, in raise_
raise exception
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/sql/schema.py", line 614, in __new__
table._init(name, metadata, *args, **kw)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/sql/schema.py", line 689, in _init
self._autoload(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/sql/schema.py", line 724, in _autoload
conn_insp.reflect_table(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/reflection.py", line 774, in reflect_table
for col_d in self.get_columns(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/reflection.py", line 497, in get_columns
col_defs = self.dialect.get_columns(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/sqlalchemy/snowdialect.py", line 669, in get_columns
schema_columns = self._get_schema_columns(connection, schema, **kw)
File "<string>", line 2, in _get_schema_columns
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/reflection.py", line 55, in cache
ret = fn(self, con, *args, **kw)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/sqlalchemy/snowdialect.py", line 479, in _get_schema_columns
schema_primary_keys = self._get_schema_primary_keys(
File "<string>", line 2, in _get_schema_primary_keys
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/reflection.py", line 55, in cache
ret = fn(self, con, *args, **kw)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/sqlalchemy/snowdialect.py", line 323, in _get_schema_primary_keys
result = connection.execute(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1385, in execute
return meth(self, multiparams, params, _EMPTY_EXECUTION_OPTS)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/sql/elements.py", line 334, in _execute_on_connection
return connection._execute_clauseelement(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1577, in _execute_clauseelement
ret = self._execute_context(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1953, in _execute_context
self._handle_dbapi_exception(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 2134, in _handle_dbapi_exception
util.raise_(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/util/compat.py", line 211, in raise_
raise exception
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/base.py", line 1910, in _execute_context
self.dialect.do_execute(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/sqlalchemy/engine/default.py", line 736, in do_execute
cursor.execute(statement, parameters)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/cursor.py", line 827, in execute
Error.errorhandler_wrapper(self.connection, self, error_class, errvalue)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/errors.py", line 275, in errorhandler_wrapper
handed_over = Error.hand_to_other_handler(
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/errors.py", line 330, in hand_to_other_handler
cursor.errorhandler(connection, cursor, error_class, error_value)
File "/home/zeusone/anaconda3/envs/snowflake_ai/lib/python3.9/site-packages/snowflake/connector/errors.py", line 209, in default_errorhandler
raise error_class(
sqlalchemy.exc.ProgrammingError: (snowflake.connector.errors.ProgrammingError) 001059 (22023): SQL compilation error:
Must specify the full search path starting from database for SNOWFLAKE_SAMPLE_DATA
[SQL: SHOW /* sqlalchemy:_get_schema_primary_keys */PRIMARY KEYS IN SCHEMA snowflake_sample_data]
(Background on this error at: https://sqlalche.me/e/14/f405)
|
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PR_kwDOIPDwls5ZXKre
| 10,105 |
Automaton Variant 2
|
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| false | null |
[] | null | 1 | 2023-09-01T18:14:48 | 2023-09-05T11:33:03 | null |
COLLABORATOR
| null |
Possible reformulation of agents
## Key Changes
1. Agents work with a [sequence of messages](https://github.com/langchain-ai/langchain/blob/29ddf63abb6a9aa67c589e2b127f7b890787f1f4/libs/langchain/langchain/automaton/typedefs.py#L79-L79) (or interface that allows fetching sequence of messages)
2. The definition of messages is expanded to include the following [internal message](https://github.com/langchain-ai/langchain/blob/29ddf63abb6a9aa67c589e2b127f7b890787f1f4/libs/langchain/langchain/automaton/typedefs.py#L39-L39) types:
* FunctionCall: name, arguments
* FunctionResult: name, result, error
* This is distinct from OpenAI function message due to encoding of result
* AgentFinish (Maybe call Yield?)
* (Existing BaseMessage)
* PrimingMessage (?) Perhaps allow user to define custom internal messages?
3. Agent implementation leverages a [generic single step chain](https://github.com/langchain-ai/langchain/blob/29ddf63abb6a9aa67c589e2b127f7b890787f1f4/libs/langchain/langchain/automaton/runnables.py#L47-L47) (aka LLMChain but memory is an input and uses a prompt generator)
4. [A Prompt Generator](https://github.com/langchain-ai/langchain/blob/29ddf63abb6a9aa67c589e2b127f7b890787f1f4/libs/langchain/langchain/automaton/runnables.py#L49-L49) is used to bridge between generalized messages and llm prompt.
## Goals
* Agents should be able to access history (e.g., chat messages)
* Agent should not take a lot of code to implement, but should be possible to fully customize
* Ideally reduce amount of code a developer needs to understand (not fully featured to not fair comparison)
## Examples
* Implementation of [Think Act](https://github.com/langchain-ai/langchain/blob/29ddf63abb6a9aa67c589e2b127f7b890787f1f4/libs/langchain/langchain/automaton/think_act_agent.py#L202) includes *priming* with a string based LLM.
* Structured chat using any [chat based LLM (Claude)](https://github.com/langchain-ai/langchain/blob/486d772184bcaffaaf92b5e2489a3985611c9bd4/libs/langchain/langchain/automaton/notebooks/chat_agent.ipynb)
* See other notebooks
## Things to note:
[ChatAgent](https://github.com/langchain-ai/langchain/pull/10105/files#diff-701c2a1ff9f13203db7396c939e9f6a15e4aa0da4f5976cdbb5e8317170a13e8R95) and [OpenAIAgent](https://github.com/langchain-ai/langchain/pull/10105/files#diff-17bae31e03cb6f1b943fe8e4df906671f2223195e88fc4275dce8b0533de7746R60) only differ in the prompt, the prompt generator and the parser
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PR_kwDOIPDwls5ZXIZb
| 10,104 |
Comprehend Moderation ver 0.1
|
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[] | null | 8 | 2023-09-01T18:05:38 | 2023-09-19T23:33:04 | 2023-09-19T23:33:04 |
CONTRIBUTOR
| null |
This PR streamlines the #9665 configuration object and logic to enable chain moderation via the Amazon Comprehend service, allowing you to detect and redact PII, Toxic, and Intent information in the LLM prompt or answer.
This implementation simplifies the configuration and types it to specific class/model.
### Usage sample
```python
from langchain_experimental.comprehend_moderation import (BaseModerationConfig,
ModerationIntentConfig,
ModerationPiiConfig,
ModerationToxicityConfig
)
pii_config = ModerationPiiConfig(
labels=["SSN"],
redact=True,
mask_character="X"
)
toxicity_config = ModerationToxicityConfig(
threshold=0.5
)
intent_config = ModerationIntentConfig(
threshold=0.5
)
moderation_config = BaseModerationConfig(
filters=[pii_config, toxicity_config, intent_config]
)
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, #specify the configuration
client=comprehend_client, #optionally pass the Boto3 Client
verbose=True
)
template = """Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here."
]
llm = FakeListLLM(responses=responses)
llm_chain = LLMChain(prompt=prompt, llm=llm)
chain = (
prompt
| comp_moderation_with_config
| {llm_chain.input_keys[0]: lambda x: x['output'] }
| llm_chain
| { "input": lambda x: x['text'] }
| comp_moderation_with_config
)
try:
response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"})
except Exception as e:
print(str(e))
else:
print(response['output'])
```
### Output
```python
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running intent Validation...
> Finished chain.
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running intent Validation...
> Finished chain.
Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like XXXXXXXXXXXX John Doe's phone number is (999)253-9876.
```
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PR_kwDOIPDwls5ZXCSR
| 10,103 |
index rename delete_mode -> cleanup
|
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[] | null | 1 | 2023-09-01T17:41:24 | 2023-09-01T18:12:11 | 2023-09-01T18:12:10 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5ZW-hT
| 10,102 |
Bagatur/eden type 2
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[] | null | 1 | 2023-09-01T17:26:22 | 2023-09-01T17:27:28 | 2023-09-01T17:27:27 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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| 1,877,781,219 |
PR_kwDOIPDwls5ZW8Dx
| 10,101 |
eden tool nb nit
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[] | null | 1 | 2023-09-01T17:16:32 | 2023-09-01T17:16:45 | 2023-09-01T17:16:39 |
COLLABORATOR
| null | null |
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| 1,877,764,117 |
I_kwDOIPDwls5v7GgV
| 10,100 |
Issue: ConversationalChatAgent reduces the user question question to single word action_input when parsing
|
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[] | null | 2 | 2023-09-01T17:01:58 | 2023-12-08T16:04:20 | 2023-12-08T16:04:19 |
NONE
| null |
### Issue you'd like to raise.
I have created an agent using ConversationalChatAgent which uses custom tools (CoversationRetrievalQA based) to answer users questions. When determining which tool to use the agent is sometimes stripping the question to single word. For example question like `What is XYZ?` is reduced to
```
{
action: tool_name,
action_input: XYZ
}
```
How can I change the behavior to include full question in this scenario?
expected behavior
```
{
action: tool_name,
action_input: What is XYZ? or Define XYZ?
}
```
### Suggestion:
_No response_
|
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I_kwDOIPDwls5v67TF
| 10,099 |
Parsing intermediate steps: Object of type AgentAction is not JSON serializable
|
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[] | null | 3 | 2023-09-01T16:30:39 | 2023-12-18T23:48:07 | 2023-12-18T23:48:07 |
NONE
| null |
### System Info
python 3.9 langchain 0.0.250
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Follow the steps in [Access Intermediate Steps](https://github.com/langchain-ai/langchain/blob/master/docs/extras/modules/agents/how_to/intermediate_steps.ipynb) within the Agent "How To".
When converting the steps to json:
print(json.dumps(response["intermediate_steps"], indent=2))
This raises the error:
TypeError: Object of type AgentAction is not JSON serializable
### Expected behavior
This issue is similar to the one raised in #8815.
However the bot answer is not satisfying as using
```
from langchain.load.dump import dumps
print(dumps(response["intermediate_steps"], pretty=True))
```
will not serialize the `AgentAction`
I can propose a `__json__()` function to correct this the lib json-fix or either inherit the class from dict
|
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bump 279
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[] | null | 1 | 2023-09-01T15:40:37 | 2023-09-01T15:49:27 | 2023-09-01T15:49:26 |
COLLABORATOR
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PR_kwDOIPDwls5ZWXrP
| 10,097 |
Temporarily remove language selection
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[] | null | 1 | 2023-09-01T15:19:05 | 2023-09-01T18:32:31 | 2023-09-01T18:30:49 |
CONTRIBUTOR
| null |
Adapting Microsoft Presidio to other languages requires a bit more work, so for now it will be good idea to remove the language option to choose, so as not to cause errors and confusion.
https://microsoft.github.io/presidio/analyzer/languages/
I will handle different languages after the weekend :smile:
|
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Created an interface for user friendly usage of HuggingFaceInferenceA…
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[] | null | 1 | 2023-09-01T14:51:55 | 2023-09-03T21:05:53 | 2023-09-03T21:05:53 |
CONTRIBUTOR
| null |
…PI to run models over API rather than locally loading it. The interface uses ChromaDB implementation
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- Dependencies: Embeddings from ChromaDB namely HuggingFaceEmbeddings,
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feat: Hugging Face Pipeline as a new Chat Model
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CONTRIBUTOR
| null |
## Description:
This Pull Request adds **Hugging Face Pipeline** as a new LangChain Chat Model.
It effectively allows the user to use any instruction tuned model with LangChain's Chat interface with a newly added Chat Templates [link](https://huggingface.co/blog/chat-templates) [link](https://huggingface.co/docs/transformers/main/chat_templating)
For example, it is now possible to define the HF Pipeline with the `meta-llama/Llama-2-7b-chat-hf` model and [arbitrary QLoRA configuration](https://huggingface.co/blog/4bit-transformers-bitsandbytes) (as shown in the [example](https://github.com/deepsense-ai/langchain/blob/llama2-chat/docs/extras/integrations/chat/huggingface_llama2.ipynb)) and run it locally with LangChain on a GPU with less than 6GB of VRAM.
@deepsense_ai
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PR_kwDOIPDwls5ZWIEI
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Data deanonymization
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[] | null | 3 | 2023-09-01T14:34:34 | 2023-09-07T04:33:25 | 2023-09-07T04:33:25 |
CONTRIBUTOR
| null |
### Description
The feature for pseudonymizing data with ability to retrieve original text (deanonymization) has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy. But then, after the model response, it would be good to have the data in the original form.
I implemented the `PresidioReversibleAnonymizer`, which consists of two parts:
1. anonymization - it works the same way as `PresidioAnonymizer`, plus the object itself stores a mapping of made-up values to original ones, for example:
```
{
"PERSON": {
"<anonymized>": "<original>",
"John Doe": "Slim Shady"
},
"PHONE_NUMBER": {
"111-111-1111": "555-555-5555"
}
...
}
```
2. deanonymization - using the mapping described above, it matches fake data with original data and then substitutes it.
Between anonymization and deanonymization user can perform different operations, for example, passing the output to LLM.
### Future works
- **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object.
- **better matching and substitution of fake values for real ones** - currently the strategy is based on matching full strings and then substituting them. Due to the indeterminism of language models, it may happen that the value in the answer is slightly changed (e.g. *John Doe* -> *John* or *Main St, New York* -> *New York*) and such a substitution is then no longer possible. Therefore, it is worth adjusting the matching for your needs.
- **Q&A with anonymization** - when I'm done writing all the functionality, I thought it would be a cool resource in documentation to write a notebook about retrieval from documents using anonymization. An iterative process, adding new recognizers to fit the data, lessons learned and what to look out for
### Twitter handle
@deepsense_ai / @MaksOpp
|
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| 1,877,270,928 |
PR_kwDOIPDwls5ZVMY_
| 10,092 |
HuggingGPT string inplace
|
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[] | null | 2 | 2023-09-01T11:42:12 | 2023-09-03T21:09:25 | 2023-09-03T21:09:25 |
CONTRIBUTOR
| null |
Description: This bug is found when using HuggingGPT in LangChain. I found that if there are dependencies between tasks, the execution results will become very bad. I debugged the code and found that it is because the filename of the generated file is not updated to the args of the following task, mainly due to the "replace()" function in string operation (it is not an in-place operation). You should assign the replace result with the string to save the change.
Issue: N/A
Dependencies: N/A
|
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| 1,877,256,103 |
PR_kwDOIPDwls5ZVJJE
| 10,091 |
Takeoff baseurl support
|
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[] | null | 1 | 2023-09-01T11:31:39 | 2023-09-03T21:46:00 | 2023-09-03T21:46:00 |
CONTRIBUTOR
| null |
## Description
This PR introduces a minor change to the TitanTakeoff integration.
Instead of specifying a port on localhost, this PR will allow users to specify a baseURL instead. This will allow users to use the integration if they have TitanTakeoff deployed externally (not on localhost). This removes the hardcoded reference to localhost "http://localhost:{port}".
### Info about Titan Takeoff
Titan Takeoff is an inference server created by [TitanML](https://www.titanml.co/) that allows you to deploy large language models locally on your hardware in a single command. Most generative model architectures are included, such as Falcon, Llama 2, GPT2, T5 and many more.
Read more about Titan Takeoff here:
- [Blog](https://medium.com/@TitanML/introducing-titan-takeoff-6c30e55a8e1e)
- [Docs](https://docs.titanml.co/docs/titan-takeoff/getting-started)
### Dependencies
No new dependencies are introduced. However, users will need to install the titan-iris package in their local environment and start the Titan Takeoff inferencing server in order to use the Titan Takeoff integration.
Thanks for your help and please let me know if you have any questions.
cc: @hwchase17 @baskaryan
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| 1,877,196,522 |
PR_kwDOIPDwls5ZU8J3
| 10,090 |
Update output_parser.py
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[] | null | 1 | 2023-09-01T10:48:56 | 2023-09-03T21:15:29 | 2023-09-03T21:15:29 |
NONE
| null |
I created a customLLM base on a llama2_70b online API service, used it for a text_to_sql mission.
I used the "create_sql_agent", it didn't work well. A piece of the logfile looks like this:
Action: sql_db_schema
Action Input: 'customers'
Observation: Error: table_names {"'customers'"} not found in database
It seemed like the llama2_70b is more likely to use single quotes, probably some other llms too.
I find out that the output_parser in "langchain\agents\mrkl\output_parser.py" strip the " " but not "'", strip them both should be a solution once for all. And it works for me.
|
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| 1,877,181,409 |
I_kwDOIPDwls5v44Ph
| 10,089 |
vector_db.similarity_search(request.query,k=2) Not working with the opensearch index
|
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[] | null | 6 | 2023-09-01T10:37:55 | 2023-12-11T16:05:23 | 2023-12-11T16:05:22 |
NONE
| null |
### System Info
I have 2000 document present at my opensearch index, i have filter out the 2 documents present from this opensearch index and added to newly created index. After that i am trying to use vector_db.similarity_search(request.query,k=2) on the newly created index, its returning me empty list.
below are the code
Code for index creation:
updated_mapping = {"mappings":{"properties": {"vector_field": {"type": "knn_vector","dimension": 1536,"method": {"engine": "nmslib","space_type": "l2","name": "hnsw","parameters": {"ef_construction": 512,"m": 16}}}}}}
opensearch_client.indices.create(index="temp_check878999", body=updated_mapping)
Code to update the index with filtered document:
sea=["1460210.pdf",'P-Reality-X Manuscript_Draft 1_17Feb22 (PAL1144).pdf']
for i in sea:
query={"query":{
"match":{
"metadata.filename":f"*{i}"
}
}}
print(query)
rest=opensearch_client.search(index="lang_demo",body=query)
create=rest['hits']['hits']
for hit in create:
sr=hit['_source']
doc_id=hit['_id']
opensearch_client.index(index="temp_check878999",id=doc_id,body=sr)
Langchain simillarity search code i am using on newly created index
from langchain.vectorstores import OpenSearchVectorSearch
vector_db = OpenSearchVectorSearch(
index_name="temp_check878999",
embedding_function=embed_wrapper(engine="text-embedding-ada-002"),
opensearch_url=*****,
http_auth=(******, *****),
is_aoss=False,
)
vector_db.similarity_search("star",k=2)
Quick reply will be very much helpful
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
No idea
### Expected behavior
It should return me the simmilarity search text with newly created index. Please note its working fine on the index where i had used vector_db.addtext(text). This issue is if i create the new open search index with opensearch client then on that index the simmilarity search one is not working.
|
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| 1,877,113,181 |
I_kwDOIPDwls5v4nld
| 10,088 |
autonomous_agents : poetry run mypy . with experimental fails with langchain version 0.0.278
|
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[] | null | 3 | 2023-09-01T09:51:26 | 2023-09-19T08:28:37 | 2023-09-19T08:28:37 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.278.
I try to publish a pull request in the project experimental. I must update the dependencies of langchain, but I receive a lot of errors.
See [here](https://github.com/langchain-ai/langchain/actions/runs/6047844508/job/16412056269?pr=7278), outside of my code.
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
In the libs/experimental/pyproject.toml, change:
langchain = ">=0.0.278"
then
poetry run mypy .
### Expected behavior
No error
|
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| 1,877,040,545 |
PR_kwDOIPDwls5ZUaQ1
| 10,087 |
fix 'Event loop is closed' error
|
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NONE
| null |
- Description: `asyncio.run()` will automatically close the event loop and trigger an error when calling `_ProactorBasePipeTransport.__del__`, whereas `asyncio.run_until_complete()` will not.
- Issue: #10086
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| 1,876,993,301 |
I_kwDOIPDwls5v4KUV
| 10,086 |
When I call the 'loader()' function of AsyncHtmlLoader, I receive an 'Event loop is closed' error after it completes execution.
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[] | null | 2 | 2023-09-01T08:31:58 | 2023-12-08T16:04:30 | 2023-12-08T16:04:29 |
NONE
| null |
### System Info
LangChain: 0.0.278
python: 3.10
windows10
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I wrote with reference to[ this link](https://python.langchain.com/docs/use_cases/web_scraping#asynchtmlloader), and the code is as follows:
```python
from langchain.document_loaders import `AsyncHtmlLoader`
urls = ['https://python.langchain.com/docs/use_cases/web_scraping#asynchtmlloader']
loader = AsyncHtmlLoader(urls)
doc = loader.load()
print(doc)
```
Return the following error after running:
```
Exception ignored in: <function _ProactorBasePipeTransport.__del__ at 0x0000023EFFD45900>
Traceback (most recent call last):
File "C:\Users\97994\AppData\Local\Programs\Python\Python310\lib\asyncio\proactor_events.py", line 116, in __del__
self.close()
File "C:\Users\97994\AppData\Local\Programs\Python\Python310\lib\asyncio\proactor_events.py", line 108, in close
self._loop.call_soon(self._call_connection_lost, None)
File "C:\Users\97994\AppData\Local\Programs\Python\Python310\lib\asyncio\base_events.py", line 750, in call_soon
self._check_closed()
File "C:\Users\97994\AppData\Local\Programs\Python\Python310\lib\asyncio\base_events.py", line 515, in _check_closed
raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed
```
### Expected behavior
None
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I_kwDOIPDwls5v4EFI
| 10,085 |
Text splitting with keep_separator is True still removes any whitespace, even if separator is whitespace
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[] | null | 4 | 2023-09-01T08:14:34 | 2023-09-08T02:01:40 | 2023-09-08T02:01:40 |
NONE
| null |
```
text = "foo _bar_ baz_ 123"
separator = "_"
text_splitter = CharacterTextSplitter(
chunk_size=4,
chunk_overlap=0,
separator="_",
keep_separator=True,
)
print(text_splitter.split_text(text))
```
RETURNS:
`['foo', '_bar', '_ baz', '_ 123']`
EXPECTED:
`['foo ', '_bar', '_ baz', '_ 123']`
^see whitespace next to `foo`
https://github.com/langchain-ai/langchain/blame/324c86acd5be9bc9d5b6dd248d686bdbb2c11cdc/libs/langchain/langchain/text_splitter.py#L155 removes all whitespace from the text. I can't figure out the purpose of this line.
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PR_kwDOIPDwls5ZUDJ1
| 10,083 |
Added code to allow handle_parsing_error to pass into the AgentExecutor
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[] | null | 2 | 2023-09-01T07:54:01 | 2023-09-03T23:10:07 | 2023-09-03T21:30:52 |
NONE
| null |
- Description: Fixed an issue with `create_pandas_dataframe_agent` where the `handle_parsing_variable` passed in does not get passed to the AgentExecutor, resulting in no difference in parsing error handling regardless of what value the variable had.
- Issue: the issue #10045
- Dependencies: No new dependencies
- Tag maintainer: @baskaryan
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I_kwDOIPDwls5v37XZ
| 10,082 |
Issue: chroma retriever where_document parameter passed in search_kwargs is invalid
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[] | null | 3 | 2023-09-01T07:52:13 | 2023-12-05T10:01:12 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I am using chroma db as retriever in ConversationalRetrievalChain, but the parameter "where_document" does not work.
```python
search_kwargs = {
"k": k,
"filter": filter,
"where_document": {"$contains": "1000001"}
}
retriever = vectordb.as_retriever(
search_kwargs=search_kwargs
)
```
In chroma official site [chroma](https://docs.trychroma.com/usage-guide), it says:
Chroma supports filtering queries by metadata and document contents. The where filter is used to filter by metadata, and the where_document filter is used to filter by document contents.
### Suggestion:
can ConversationalRetrievalChain support where_document filter for chroma db?
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I_kwDOIPDwls5v36tQ
| 10,081 |
Vectara query a already uploaded Corpus
|
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"node_id": "LA_kwDOIPDwls8AAAABSkuNyg",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/area:%20vector%20store",
"name": "area: vector store",
"color": "D4C5F9",
"default": false,
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| false | null |
[] | null | 1 | 2023-09-01T07:50:13 | 2023-12-08T16:04:35 | 2023-12-08T16:04:34 |
NONE
| null |
### Issue you'd like to raise.
I have uploaded my files to Vectara and would love to query the corpus now with langchain. However I can only find examples of how to upload documents and then directly query them. I would like to avoid uploading the documents all the time and just straight query the existing corpus . Is this possible ?
Thank you so much!
Regards
### Suggestion:
_No response_
|
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| 1,876,911,576 |
I_kwDOIPDwls5v32XY
| 10,080 |
StructuredTool ainvoke isn't await parent class ainvoke
|
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| false | null |
[] | null | 0 | 2023-09-01T07:36:50 | 2023-09-08T02:54:54 | 2023-09-08T02:54:54 |
NONE
| null |
[code pointer](https://github.com/langchain-ai/langchain/blob/74fcfed4e2bdd186c2869a07008175a9b66b1ed4/libs/langchain/langchain/tools/base.py#L588C16-L588C16)
In `langchain.tools.base`, change
```python
Class StructuredTool(BaseTool):
"""Tool that can operate on any number of inputs."""
description: str = ""
args_schema: Type[BaseModel] = Field(..., description="The tool schema.")
"""The input arguments' schema."""
func: Optional[Callable[..., Any]]
"""The function to run when the tool is called."""
coroutine: Optional[Callable[..., Awaitable[Any]]] = None
"""The asynchronous version of the function."""
# --- Runnable ---
async def ainvoke(
self,
input: Union[str, Dict],
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> Any:
if not self.coroutine:
# If the tool does not implement async, fall back to default implementation
return await asyncio.get_running_loop().run_in_executor(
None, partial(self.invoke, input, config, **kwargs)
)
return super().ainvoke(input, config, **kwargs)
```
to
```python
Class StructuredTool(BaseTool):
"""Tool that can operate on any number of inputs."""
description: str = ""
args_schema: Type[BaseModel] = Field(..., description="The tool schema.")
"""The input arguments' schema."""
func: Optional[Callable[..., Any]]
"""The function to run when the tool is called."""
coroutine: Optional[Callable[..., Awaitable[Any]]] = None
"""The asynchronous version of the function."""
# --- Runnable ---
async def ainvoke(
self,
input: Union[str, Dict],
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> Any:
if not self.coroutine:
# If the tool does not implement async, fall back to default implementation
return await asyncio.get_running_loop().run_in_executor(
None, partial(self.invoke, input, config, **kwargs)
)
return await super().ainvoke(input, config, **kwargs)
```
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| 1,876,788,507 |
I_kwDOIPDwls5v3YUb
| 10,079 |
how to use PythonREPL tool to take dataframe and query
|
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[] | null | 3 | 2023-09-01T05:51:51 | 2023-12-08T16:04:40 | 2023-12-08T16:04:39 |
NONE
| null |
### Issue you'd like to raise.
I need to use Python REPL tool to take data frame and user query and answer based on the data frame.
### Suggestion:
_No response_
|
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| 1,876,763,580 |
I_kwDOIPDwls5v3SO8
| 10,078 |
ConversationTokenBufferMemory and ConversationSummaryBufferMemory does not work with db-backed histories
|
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open
| false | null |
[] | null | 2 | 2023-09-01T05:28:56 | 2023-10-09T11:56:36 | null |
NONE
| null |
### System Info
the memories are pruned after saving using .pop(0). However, db-backed histories read messages and copies into list each turn. This makes it so that the actual db does not change at every turn, and so max_token_limit parameter gets ignored and the memory prints out the entire conversation for history.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Initialize redis chat history
use redis chat history as chat_history for ConversationSummaryBufferMemory
set max_token_limit to 1.
Print history at every turn.
Still prints the entire history
### Expected behavior
Initialize redis chat history
use redis chat history as chat_history for ConversationSummaryBufferMemory
set max_token_limit to 1.
Print history at every turn.
Print nothing since max_token_limit = 0
|
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| 1,876,736,736 |
I_kwDOIPDwls5v3Lrg
| 10,077 |
ElasticSearch authentication
|
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[] | null | 2 | 2023-09-01T05:07:53 | 2023-12-08T16:04:45 | 2023-12-08T16:04:44 |
NONE
| null |
### System Info
On the elasticsearch authentication, you have implemented it this way on the elasticsearch.py file found in the vectorstores folder
```py
if api_key:
connection_params["api_key"] = api_key
elif username and password:
connection_params["basic_auth"] = (username, password)
```
but i think it should be this way
```py
if api_key:
connection_params["api_key"] = api_key
elif username and password:
connection_params["http_auth"] = (username, password)
```
with that change the authentication succeeeds. What i have just changed is this connection_params["basic_auth"] = (username, password) to this connection_params["http_auth"] = (username, password)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create an elasticsearch instance on an EC2 instance using docker that has SSL and also uses username and password and then try to authenticate to that elasticsearch instance using langchain you will see the error
### Expected behavior
The expected behavior is successfull authentication
|
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| 1,876,599,890 |
PR_kwDOIPDwls5ZS7mW
| 10,076 |
fix typo in web_research.py
|
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[] | null | 1 | 2023-09-01T02:14:23 | 2023-09-01T05:19:04 | 2023-09-01T05:19:04 |
CONTRIBUTOR
| null |
fix spelling
|
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| 1,876,514,876 |
PR_kwDOIPDwls5ZSooQ
| 10,074 |
use case docs reorder
|
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[] | null | 1 | 2023-09-01T01:02:35 | 2023-09-05T22:11:18 | 2023-09-05T22:11:16 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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| 1,876,503,478 |
PR_kwDOIPDwls5ZSmBh
| 10,073 |
Mv LCEL docs up a level
|
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[] | null | 1 | 2023-09-01T00:55:34 | 2023-09-01T15:20:56 | 2023-09-01T15:20:55 |
COLLABORATOR
| null | null |
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| 1,876,474,014 |
I_kwDOIPDwls5v2Lie
| 10,072 |
Receiving a unclear KeyError: 'error' when using Langchain HuggingFaceTextInference on Runpod GPU
|
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[] | null | 2 | 2023-09-01T00:24:57 | 2023-12-08T16:04:50 | 2023-12-08T16:04:49 |
NONE
| null |
### System Info
Langchain version: `0.0278`
Python: `3.10`
Runpod version: `1.2.0`
I'm experiencing some issues when running Runpod (TGI gpu cloud) and langchain, primarily when I try to run the chain.
For reference, I'm using TheBloke/VicUnlocked-30B-LoRA-GPTQ model in TGI on Runpod A4500 GPU cloud.
I initialize the pod from the UI and connect to it with the runpod-python library (version 1.2.0) in my python 3.10 environment.
My prompt template is as follows:
```
prompt = PromptTemplate(
input_variables=['instruction','summary'],
template="""### Instruction:
{instruction}
### Input:
{summary}
### Response:
""")
```
The instruction is a simple instruction to extract relevant insights from a summary. My LLM is instantiated as such:
inference_server_url = f'https://{pod["id"]}-{port}.proxy.runpod.net' ### note: the pod and port is defined previously.
llm = HuggingFaceTextGenInference(inference_server_url=inference_server_url)
And I am trying to run the model as such:
```
summary = ... # summary here
instruction = ... #instruction here
chain.run({"instruction": instruction, "summary": summary}) #**_Note: Error occurs from this line!!_**
```
But I get this error:
```
File ~/anaconda3/envs/py310/lib/python3.10/site-packages/langchain/chains/base.py:282, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
280 except (KeyboardInterrupt, Exception) as e:
281 run_manager.on_chain_error(e)
--> 282 raise e
283 run_manager.on_chain_end(outputs)
284 final_outputs: Dict[str, Any] = self.prep_outputs(
285 inputs, outputs, return_only_outputs
...
---> 81 message = payload["error"]
82 if "error_type" in payload:
83 error_type = payload["error_type"]
KeyError: 'error'
```
Any ideas?
### 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
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
My prompt template is as follows:
prompt = PromptTemplate(
input_variables=['instruction','summary'],
template="""### Instruction:
{instruction}
### Input:
{summary}
### Response:
""")
The instruction is a simple instruction to extract relevant insights from a summary. My LLM is instantiated as such:
inference_server_url = f'https://{pod["id"]}-{port}.proxy.runpod.net' ### note: the pod and port is defined previously.
llm = HuggingFaceTextGenInference(inference_server_url=inference_server_url)
And I am trying to run the model as such:
summary = ... # summary here
instruction = ... #instruction here
chain.run({"instruction": instruction, "summary": summary})
And then running chain.run I get the error as mentioned above.
### Expected behavior
What's expected is that I should be receiving the output from the runpod GPU cloud that is hosting the model, as per this guide that I am following:
https://colab.research.google.com/drive/10BJcKRBtMlpm2hsS2antarSRgEQY3AQq#scrollTo=lyVYLW2thTMg
|
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| 1,876,449,621 |
PR_kwDOIPDwls5ZSapt
| 10,071 |
add repr for not serializable
|
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[] | null | 1 | 2023-08-31T23:51:16 | 2023-09-01T16:18:34 | 2023-09-01T16:18:33 |
COLLABORATOR
| null | null |
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| 1,876,399,968 |
PR_kwDOIPDwls5ZSQZl
| 10,070 |
Comprehend Moderation 0.1
|
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[] | null | 5 | 2023-08-31T22:35:13 | 2023-09-01T23:01:28 | 2023-09-01T23:01:06 |
CONTRIBUTOR
| null |
This PR streamlines the #9665 configuration object and logic to enable chain moderation via the Amazon Comprehend service, allowing you to detect and redact PII, Toxic, and Intent information in the LLM prompt or answer.
This implementation simplifies the configuration and types it to specific class/model.
### Usage sample
```python
from langchain_experimental.comprehend_moderation import (BaseModerationConfig,
ModerationIntentConfig,
ModerationPiiConfig,
ModerationToxicityConfig
)
pii_config = ModerationPiiConfig(
labels=["SSN"],
redact=True,
mask_character="X"
)
toxicity_config = ModerationToxicityConfig(
threshold=0.5
)
intent_config = ModerationIntentConfig(
threshold=0.5
)
moderation_config = BaseModerationConfig(
filters=[pii_config, toxicity_config, intent_config]
)
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, #specify the configuration
client=comprehend_client, #optionally pass the Boto3 Client
verbose=True
)
template = """Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here."
]
llm = FakeListLLM(responses=responses)
llm_chain = LLMChain(prompt=prompt, llm=llm)
chain = (
prompt
| comp_moderation_with_config
| {llm_chain.input_keys[0]: lambda x: x['output'] }
| llm_chain
| { "input": lambda x: x['text'] }
| comp_moderation_with_config
)
try:
response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"})
except Exception as e:
print(str(e))
else:
print(response['output'])
```
### Output
```python
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running intent Validation...
> Finished chain.
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running intent Validation...
> Finished chain.
Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like XXXXXXXXXXXX John Doe's phone number is (999)253-9876.
```
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PR_kwDOIPDwls5ZSDex
| 10,069 |
Add example of running Q&A over structured data using the `Airbyte` loaders and `pandas`
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[] | null | 4 | 2023-08-31T21:27:33 | 2023-09-03T21:32:33 | 2023-09-03T21:32:33 |
CONTRIBUTOR
| null |
- Description: Added example of running Q&A over structured data using the `Airbyte` loaders and `pandas`
- Dependencies: any dependencies required for this change,
- Tag maintainer: @hwchase17
- Twitter handle: @pelaseyed
|
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PR_kwDOIPDwls5ZRs6R
| 10,067 |
feat: Add `ApifyWrapper` class
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[] | null | 3 | 2023-08-31T20:16:02 | 2023-08-31T22:49:28 | 2023-08-31T22:47:44 |
CONTRIBUTOR
| null |
If you look at documentation https://python.langchain.com/docs/integrations/tools/apify (or the actual file https://github.com/langchain-ai/langchain/blob/master/docs/extras/integrations/tools/apify.ipynb ), there's a class `ApifyWrapper` mentioned. It seems it got lost in some refactoring, i.e. it does not exist in the codebase ATM.
I just propose to add it back.
It would fix issues e.g. https://github.com/langchain-ai/langchain/issues/8307 or https://github.com/langchain-ai/langchain/issues/8201
To add, Apify is a wanted integration, e.g. see https://twitter.com/hwchase17/status/1695490295914545626 or https://twitter.com/hwchase17/status/1695470765343461756
Lastly, I offer taking ownership of the Apify-related parts of the codebase, so you can tag me if anything is needed.
|
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PR_kwDOIPDwls5ZRRRv
| 10,066 |
docs: agents & callbacks fixes
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[] | null | 3 | 2023-08-31T18:54:25 | 2023-09-01T20:34:33 | 2023-09-01T20:28:55 |
CONTRIBUTOR
| null |
Various improvements to the Agents & Callbacks sections of the documentation including formatting, spelling, and grammar fixes to improve readability.
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I_kwDOIPDwls5v0lbv
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SupabaseVectorStore: Error thrown when calling similarity_search_by_vector_with_relevance_scores
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[] | null | 6 | 2023-08-31T18:18:02 | 2023-12-07T16:05:15 | 2023-12-07T16:05:14 |
NONE
| null |
### System Info
### Error
I am using **Supabase Vector Store**:
```python
embeddings = OpenAIEmbeddings()
vectorstore_public = SupabaseVectorStore(
client=supabase_client,
embedding=embeddings,
table_name="documents",
query_name="match_documents",
)
```
And crawling pages from internet using the `WebResearchRetriever`
But, in `WebResearchRetriever._get_relevant_documents`
```python
# Search for relevant splits
# TODO: make this async
logger.info("Grabbing most relevant splits from urls...")
docs = []
for query in questions:
docs.extend(self.vectorstore.similarity_search(query))
```
The `vectorstore.similarity_search` guides to an rpc call using the `SupabaseVectorStore`
```python
def similarity_search_by_vector_with_relevance_scores(
self, query: List[float], k: int, filter: Optional[Dict[str, Any]] = None
) -> List[Tuple[Document, float]]:
match_documents_params = self.match_args(query, k, filter)
print("match_documents_params", match_documents_params)
print("self.query_name", self.query_name)
res = self._client.rpc(self.query_name, match_documents_params).execute() # here is where the error is thrown
print("res", res)
match_result = [
(
Document(
metadata=search.get("metadata", {}), # type: ignore
page_content=search.get("content", ""),
),
search.get("similarity", 0.0),
)
for search in res.data
if search.get("content")
]
return match_result
```
Error thrown:
```python
File "/lib/python3.10/site-packages/postgrest/_sync/request_builder.py", line 68, in execute
raise APIError(r.json())
postgrest.exceptions.APIError: {'code': '42804', 'details': 'Returned type text does not match expected type bigint in column 1.', 'hint': None, 'message': 'structure of query does not match function result type'}
```
Complete Traceback
```python
Traceback (most recent call last):
File "/lib/python3.10/site-packages/flask/app.py", line 2190, in wsgi_app
response = self.full_dispatch_request()
File "/lib/python3.10/site-packages/flask/app.py", line 1486, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/lib/python3.10/site-packages/flask_cors/extension.py", line 176, in wrapped_function
return cors_after_request(app.make_response(f(*args, **kwargs)))
File "/lib/python3.10/site-packages/flask/app.py", line 1484, in full_dispatch_request
rv = self.dispatch_request()
File "/lib/python3.10/site-packages/flask/app.py", line 1469, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
File "/app.py", line 143, in complete
answer = completion(message)
File "/methods/content_completion.py", line 15, in completion
sources = web_explorer(blog_topic)
File "/methods/web_explorer.py", line 93, in web_explorer
result = retrieve_answer_and_sources(question, llm, web_retriever)
File "/methods/web_explorer.py", line 80, in retrieve_answer_and_sources
return qa_chain({"question": question}, return_only_outputs=True)
File "/lib/python3.10/site-packages/langchain/chains/base.py", line 288, in __call__
raise e
File "/lib/python3.10/site-packages/langchain/chains/base.py", line 282, in __call__
self._call(inputs, run_manager=run_manager)
File "/lib/python3.10/site-packages/langchain/chains/qa_with_sources/base.py", line 151, in _call
docs = self._get_docs(inputs, run_manager=_run_manager)
File "/lib/python3.10/site-packages/langchain/chains/qa_with_sources/retrieval.py", line 50, in _get_docs
docs = self.retriever.get_relevant_documents(
File "/lib/python3.10/site-packages/langchain/schema/retriever.py", line 208, in get_relevant_documents
raise e
File "/lib/python3.10/site-packages/langchain/schema/retriever.py", line 201, in get_relevant_documents
result = self._get_relevant_documents(
File "/methods/retrievers.py", line 252, in _get_relevant_documents
docs.extend(self.vectorstore.similarity_search(query))
File "/lib/python3.10/site-packages/langchain/vectorstores/supabase.py", line 172, in similarity_search
return self.similarity_search_by_vector(
File "/lib/python3.10/site-packages/langchain/vectorstores/supabase.py", line 183, in similarity_search_by_vector
result = self.similarity_search_by_vector_with_relevance_scores(
File "/lib/python3.10/site-packages/langchain/vectorstores/supabase.py", line 217, in similarity_search_by_vector_with_relevance_scores
res = self._client.rpc(self.query_name, match_documents_params).execute()
File "/lib/python3.10/site-packages/postgrest/_sync/request_builder.py", line 68, in execute
raise APIError(r.json())
postgrest.exceptions.APIError: {'code': '42804', 'details': 'Returned type text does not match expected type bigint in column 1.', 'hint': None, 'message': 'structure of query does not match function result type'}
```
requirements.txt to reproduce:
```
aiohttp==3.8.5
aiosignal==1.3.1
anyio==4.0.0
async-timeout==4.0.3
attrs==23.1.0
azure-core==1.29.3
backoff==2.2.1
bcrypt==4.0.1
beautifulsoup4==4.12.2
bleach==6.0.0
blinker==1.6.2
cachetools==5.3.1
certifi==2023.7.22
chardet==5.2.0
charset-normalizer==3.2.0
chroma-hnswlib==0.7.2
chromadb==0.4.8
click==8.1.7
click-log==0.4.0
colorama==0.4.6
coloredlogs==15.0.1
dataclasses-json==0.5.14
deprecation==2.1.0
dnspython==2.4.2
docutils==0.20.1
dotty-dict==1.3.1
emoji==2.8.0
exceptiongroup==1.1.3
fastapi==0.99.1
filetype==1.2.0
Flask==2.3.3
Flask-Cors==4.0.0
flatbuffers==23.5.26
frozenlist==1.4.0
gitdb==4.0.10
GitPython==3.1.32
google-api-core==2.11.1
google-api-python-client==2.97.0
google-auth==2.22.0
google-auth-httplib2==0.1.0
googleapis-common-protos==1.60.0
gotrue==1.0.4
h11==0.14.0
html-sanitizer==2.2.0
httpcore==0.16.3
httplib2==0.22.0
httptools==0.6.0
httpx==0.23.3
humanfriendly==10.0
idna==3.4
importlib-metadata==6.8.0
importlib-resources==6.0.1
invoke==1.7.3
itsdangerous==2.1.2
jaraco.classes==3.3.0
Jinja2==3.1.2
joblib==1.3.2
keyring==24.2.0
langchain==0.0.277
langsmith==0.0.30
lxml==4.9.3
Markdown==3.4.4
MarkupSafe==2.1.3
marshmallow==3.20.1
monotonic==1.6
more-itertools==10.1.0
mpmath==1.3.0
multidict==6.0.4
mypy-extensions==1.0.0
nltk==3.8.1
numexpr==2.8.5
numpy==1.25.2
onnxruntime==1.15.1
openai==0.27.10
outcome==1.2.0
overrides==7.4.0
packaging==23.1
pkginfo==1.9.6
postgrest==0.10.6
posthog==3.0.2
protobuf==4.24.2
pulsar-client==3.3.0
pyasn1==0.5.0
pyasn1-modules==0.3.0
pydantic==1.10.12
Pygments==2.16.1
pymongo==4.5.0
pyparsing==3.1.1
PyPika==0.48.9
PySocks==1.7.1
python-dateutil==2.8.2
python-dotenv==1.0.0
python-gitlab==3.15.0
python-magic==0.4.27
python-semantic-release==7.33.2
PyYAML==6.0.1
readme-renderer==41.0
realtime==1.0.0
regex==2023.8.8
requests==2.31.0
requests-toolbelt==1.0.0
rfc3986==1.5.0
rsa==4.9
selenium==4.11.2
semver==2.13.0
six==1.16.0
smmap==5.0.0
sniffio==1.3.0
sortedcontainers==2.4.0
soupsieve==2.4.1
SQLAlchemy==2.0.20
starlette==0.27.0
storage3==0.5.3
StrEnum==0.4.15
supabase==1.0.3
supafunc==0.2.2
sympy==1.12
tabulate==0.9.0
tenacity==8.2.3
tiktoken==0.4.0
tokenizers==0.13.3
tomlkit==0.12.1
tqdm==4.66.1
trio==0.22.2
trio-websocket==0.10.3
twine==3.8.0
typing-inspect==0.9.0
typing_extensions==4.7.1
unstructured==0.10.10
uritemplate==4.1.1
urllib3==1.26.16
uvicorn==0.23.2
uvloop==0.17.0
waitress==2.1.2
watchfiles==0.20.0
webencodings==0.5.1
websockets==10.4
Werkzeug==2.3.7
wsproto==1.2.0
yarl==1.9.2
zipp==3.16.2
```
### Who can help?
@agola11 @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code from this repo: https://github.com/langchain-ai/web-explorer/blob/main/web_explorer.py
But instead of faiss, use `SupabaseVectorStore` as above.
### Expected behavior
Is it coming from the config of my table on Supabase, as i get: `Returned type text does not match expected type bigint in column 1.` or wherever else ?
<img width="1176" alt="Capture d’écran 2023-08-31 à 20 15 58" src="https://github.com/langchain-ai/langchain/assets/39488794/743d9cd9-fa30-474d-88ce-288e85c50e71">
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| 1,876,055,216 |
I_kwDOIPDwls5v0lSw
| 10,064 |
Add SelfQueryRetriever support for Redis Vector Stores
|
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[] | null | 1 | 2023-08-31T18:17:38 | 2023-09-12T22:30:38 | 2023-09-12T22:30:38 |
NONE
| null |
### Feature request
Implement Self Query retriever to Redis vector stores.
### Motivation
I was trying the different retrievers for my project (AI chatbot that answers various human resources and labor law questions based on a dataset built from complete local labor legislation, company handbooks, job descriptions and SOPs).
I plan to deploy this chatbot in a production environment, therefore I chose Redis (for its robustness and speed) as a vector store.
### Your contribution
I am not a pro developer; so, unfortunately, the only contributions I can make is limited to "real-world" testing.
|
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| 10,062 |
Data anonymizer notebook nit
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I_kwDOIPDwls5v0cMT
| 10,061 |
CustomLLM when called inside agent does not return custom behaviour
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[] | null | 2 | 2023-08-31T17:53:26 | 2023-12-07T16:05:20 | 2023-12-07T16:05:19 |
NONE
| null |
### System Info
Langchain 0.0.276
Windows
Python 3.11.4
I create a custom llm just like in the tutorial but when i use it on a mkrl agent it does not return to the agent the custom response.
I printed insided my agent and it is called and returns the correct answer, but the agent in the end of line does not get this answer.
### Who can help?
@hwchase17
@agola11
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://colab.research.google.com/drive/1cHVx3GlzmF4ECV_63ZjlrMPt8P840GSw?usp=sharing
### Expected behavior
The expected behaviour is the agent to receive the response of the CustomLLM.
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[] | null | 6 | 2023-08-31T17:02:38 | 2023-12-11T16:05:28 | 2023-12-11T16:05:27 |
NONE
| null |
### System Info
Langchain version -- 0.0.277
python verion -- 3.8.8
window platform
### Who can help?
@hwchase17
@agola11
Hi I am not able to run any of the langchain syntex on my windows laptop. For example if I just run from **langchain.llms import OpenAI** I get the below error message. Can you please help!
TypeError Traceback (most recent call last)
<ipython-input-3-5af9a0f5ffa4> in <module>
1 import os
2 from dotenv import load_dotenv
----> 3 from langchain.llms import OpenAI
~\Anaconda3\lib\site-packages\langchain\__init__.py in <module>
4 from typing import Optional
5
----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
7 from langchain.chains import (
8 ConversationChain,
~\Anaconda3\lib\site-packages\langchain\agents\__init__.py in <module>
29
30 """ # noqa: E501
---> 31 from langchain.agents.agent import (
32 Agent,
33 AgentExecutor,
~\Anaconda3\lib\site-packages\langchain\agents\agent.py in <module>
12 import yaml
13
---> 14 from langchain.agents.agent_iterator import AgentExecutorIterator
15 from langchain.agents.agent_types import AgentType
16 from langchain.agents.tools import InvalidTool
~\Anaconda3\lib\site-packages\langchain\agents\agent_iterator.py in <module>
19 )
20
---> 21 from langchain.callbacks.manager import (
22 AsyncCallbackManager,
23 AsyncCallbackManagerForChainRun,
~\Anaconda3\lib\site-packages\langchain\callbacks\__init__.py in <module>
8 """
9
---> 10 from langchain.callbacks.aim_callback import AimCallbackHandler
11 from langchain.callbacks.argilla_callback import ArgillaCallbackHandler
12 from langchain.callbacks.arize_callback import ArizeCallbackHandler
~\Anaconda3\lib\site-packages\langchain\callbacks\aim_callback.py in <module>
3
4 from langchain.callbacks.base import BaseCallbackHandler
----> 5 from langchain.schema import AgentAction, AgentFinish, LLMResult
6
7
~\Anaconda3\lib\site-packages\langchain\schema\__init__.py in <module>
1 """**Schemas** are the LangChain Base Classes and Interfaces."""
2 from langchain.schema.agent import AgentAction, AgentFinish
----> 3 from langchain.schema.cache import BaseCache
4 from langchain.schema.chat_history import BaseChatMessageHistory
5 from langchain.schema.document import BaseDocumentTransformer, Document
~\Anaconda3\lib\site-packages\langchain\schema\cache.py in <module>
4 from typing import Any, Optional, Sequence
5
----> 6 from langchain.schema.output import Generation
7
8 RETURN_VAL_TYPE = Sequence[Generation]
~\Anaconda3\lib\site-packages\langchain\schema\output.py in <module>
7 from langchain.load.serializable import Serializable
8 from langchain.pydantic_v1 import BaseModel, root_validator
----> 9 from langchain.schema.messages import BaseMessage, BaseMessageChunk
10
11
~\Anaconda3\lib\site-packages\langchain\schema\messages.py in <module>
146
147
--> 148 class HumanMessageChunk(HumanMessage, BaseMessageChunk):
149 """A Human Message chunk."""
150
~\Anaconda3\lib\site-packages\pydantic\main.cp38-win_amd64.pyd in pydantic.main.ModelMetaclass.__new__()
~\Anaconda3\lib\abc.py in __new__(mcls, name, bases, namespace, **kwargs)
83 """
84 def __new__(mcls, name, bases, namespace, **kwargs):
---> 85 cls = super().__new__(mcls, name, bases, namespace, **kwargs)
86 _abc_init(cls)
87 return cls
TypeError: multiple bases have instance lay-out conflict
### 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
!pip -q install openai langchain huggingface_hub
import openai
import dotenv
import os
os.environ['OPENAI_API_KEY'] = ' ... '
from langchain.llms import OpenAI
### Expected behavior
Should be able to run without error
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I_kwDOIPDwls5vz52i
| 10,059 |
max_marginal_relevance_search not implemented in Redis
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[] | null | 4 | 2023-08-31T16:34:30 | 2023-09-12T22:31:16 | 2023-09-12T22:31:16 |
NONE
| null |
### System Info
LangChain version: 0.0.276
Python version: 3.11.2
Platform: x86_64 Debian 12.2.0-14
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import os, openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Redis
embeddings = OpenAIEmbeddings()
rds = Redis.from_existing_index(
embeddings,
index_name = INDEX_NAME,
schema = "redis_schema_kws.yaml",
redis_url = "redis://10.0.1.21:6379",
)
returned_docs_mmr = rds.max_marginal_relevance_search(question, k=3, fetch_k=3, lambda_mult=0.8)
```
The returned error message is: `NotImplementedError:`
Could you provide additional information on your ETA to have MMR search implemented in Redis?
### Expected behavior
Retrieve the Document objects.
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PR_kwDOIPDwls5ZQcL8
| 10,058 |
add async support for tools
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| 1,875,844,490 |
I_kwDOIPDwls5vzx2K
| 10,057 |
PydanticOutputParser failing to parse basic string into JSON
|
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[] | null | 7 | 2023-08-31T16:12:05 | 2024-01-03T08:52:32 | null |
NONE
| null |
### System Info
Given a string resembling a JSON-string array, such as I'm often getting from LLM completions, I'm trying to convert the string to a Python list of strings. I'm finding the the `PydanticOutputParser` is throwing an error when plain old `json.loads` with `strict=False` is doing fine.
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
For example this code:
```python
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field
import json
class Lines(BaseModel):
lines: list[str] = Field(description="array of strings")
line_parser = PydanticOutputParser(pydantic_object=Lines)
lines = '[\n "line 1",\n "line 2"\n]'
print("Just JSON:")
print(json.loads(lines, strict=False))
print("LangChain Pydantic:")
print(line_parser.parse(lines))
```
produces the following output:
```sh
Just JSON:
['line 1', 'line 2']
LangChain Pydantic:
Traceback (most recent call last):
File "/opt/miniconda3/envs/gt/lib/python3.10/site-packages/langchain/output_parsers/pydantic.py", line 27, in parse
json_object = json.loads(json_str, strict=False)
File "/opt/miniconda3/envs/gt/lib/python3.10/json/__init__.py", line 359, in loads
return cls(**kw).decode(s)
File "/opt/miniconda3/envs/gt/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/opt/miniconda3/envs/gt/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/benjaminbasseri/Dropbox/Work/GT Schools/scratch work/meme-generation/parse_test.py", line 14, in <module>
print(line_parser.parse(lines))
File "/opt/miniconda3/envs/gt/lib/python3.10/site-packages/langchain/output_parsers/pydantic.py", line 33, in parse
raise OutputParserException(msg, llm_output=text)
langchain.schema.output_parser.OutputParserException: Failed to parse Lines from completion [
"line 1",
"line 2"
]. Got: Expecting value: line 1 column 1 (char 0)
```
### Expected behavior
I would expect the parser to be able to handle a simple case like this
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| 1,875,844,026 |
PR_kwDOIPDwls5ZQWFT
| 10,056 |
Fixing _InactiveRpcError in MatchingEngine vectorstore
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[] | null | 5 | 2023-08-31T16:11:43 | 2023-09-19T23:16:05 | 2023-09-19T23:16:04 |
CONTRIBUTOR
| null |
- Description: There was an issue with the MatchingEngine VectorStore, preventing from using it with a public endpoint. In the Google Cloud library there are two similar methods for private or public endpoints : `match()` and `find_neighbors()`.
- Issue: Fixes #8378
- This uses the `google.cloud.aiplatform` library : https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/matching_engine/matching_engine_index_endpoint.py
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PR_kwDOIPDwls5ZQLlM
| 10,055 |
Update chroma.py
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[] | null | 3 | 2023-08-31T15:44:24 | 2023-09-03T21:35:14 | 2023-09-03T21:35:14 |
NONE
| null |
- Description: add minor fix codes in langchain.vectorstors.chroma to return distances value .
so
qa=RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever, #a chroma db
return_source_documents=True,
chain_type_kwargs={"prompt": prompt, "memory": memory},
)
res = qa(query)
Now res can return distances value in the metadata dict.
answer, docs = res["result"], res["source_documents"]
for document in docs:
print("\n> [source file path]: " + document.metadata["source"] )
print("> [cosine similarity]:" + str(1.0-document.metadata["distances"]) ) # Add This and it will print 1-distance_value
print(">[text slice]:" + document.page_content)
It is a very minor change. I tested on the [https://github.com/PromtEngineer/localGPT/blob/main/run_localGPT.py#L237]
- Issue: the issue #4710 #5416 Maybe this fix is not directly with the issues , but I guess the intuition is alike.
- Dependencies: same as the main branch
- Tag maintainer: @baskaryan, @eyurtsev, @hwchase17, @rlancemartin
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I_kwDOIPDwls5vzfra
| 10,054 |
StreamingStdOutCallbackHandler().on_llm_start isn't called since version 0.0.272 (0.0.271 still works)
|
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[] | null | 4 | 2023-08-31T15:30:31 | 2024-01-10T09:15:32 | null |
NONE
| null |
### System Info
0.0.272, linux, python 3.11.2
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
I have a pretty standard RetievalQA chain like this. The `on_llm_start` callback isn't executed since version 0.0.272 (I verified that 0.0.271 is working)
```
loader = TextLoader("data.txt")
documents = loader.load()
openai = ChatOpenAI(**llm_params, callbacks= [StreamingStdOutCallbackHandler()])
text_splitter = RecursiveCharacterTextSplitter(**chunking_params)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
qa = RetrievalQA.from_chain_type(
llm=openai,
retriever=docsearch.as_retriever(),
)
```
### Expected behavior
`on_llm_start` callback should be called
|
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PR_kwDOIPDwls5ZPsi_
| 10,052 |
bump 278
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PR_kwDOIPDwls5ZPmSr
| 10,051 |
Add XMLOutputParser
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[] | null | 7 | 2023-08-31T14:13:14 | 2023-09-19T23:17:34 | 2023-09-19T23:17:34 |
CONTRIBUTOR
| null |
**Description**
Adds new output parser, this time enabling the output of LLM to be of an XML format. Seems to be particularly useful together with Claude model.
Addresses [issue 9820](https://github.com/langchain-ai/langchain/issues/9820).
**Twitter handle**
@deepsense_ai @matt_wosinski
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| 1,875,605,668 |
I_kwDOIPDwls5vy3ik
| 10,050 |
langchain.vectorstores.MatchingEngine.from_components() throws InvalidArgument exception
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[] | null | 4 | 2023-08-31T14:01:53 | 2023-12-30T16:01:55 | null |
NONE
| null |
### System Info
langchain==0.0.249
Windows
Python 3.11.4
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The official example described here:
https://python.langchain.com/docs/integrations/vectorstores/matchingengine#create-vectorstore-from-texts
Does not work.
My code throws beloe exception:
from langchain.vectorstores import MatchingEngine
texts = [
"The cat sat on",
"the mat.",
"I like to",
"eat pizza for",
"dinner.",
"The sun sets",
"in the west.",
]
vector_store = MatchingEngine.from_components(
project_id="",
region="",
gcs_bucket_name="",
index_id="",
endpoint_id="",
)
### Expected behavior
Traceback (most recent call last):
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\google\api_core\grpc_helpers.py", line 72, in error_remapped_callable
return callable_(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\grpc\_channel.py", line 1030, in __call__
return _end_unary_response_blocking(state, call, False, None)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\grpc\_channel.py", line 910, in _end_unary_response_blocking
raise _InactiveRpcError(state) # pytype: disable=not-instantiable
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.INVALID_ARGUMENT
details = "Request contains an invalid argument."
debug_error_string = "UNKNOWN:Error received from peer ipv4:172.217.16.42:443 {created_time:"2023-08-31T14:00:16.506362524+00:00", grpc_status:3, grpc_message:"Request contains an invalid argument."}"
>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\GitHub\onecloud-chatbot\matching_engine_insert.py", line 13, in <module>
vector_store = MatchingEngine.from_components(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\langchain\vectorstores\matching_engine.py", line 280, in from_components
endpoint = cls._create_endpoint_by_id(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\langchain\vectorstores\matching_engine.py", line 386, in _create_endpoint_by_id
return aiplatform.MatchingEngineIndexEndpoint(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\google\cloud\aiplatform\matching_engine\matching_engine_index_endpoint.py", line 130, in __init__
self._gca_resource = self._get_gca_resource(resource_name=index_endpoint_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\google\cloud\aiplatform\base.py", line 648, in _get_gca_resource
return getattr(self.api_client, self._getter_method)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\google\cloud\aiplatform_v1\services\index_endpoint_service\client.py", line 707, in get_index_endpoint
response = rpc(
^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\google\api_core\gapic_v1\method.py", line 113, in __call__
return wrapped_func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\google\api_core\retry.py", line 349, in retry_wrapped_func
return retry_target(
^^^^^^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\google\api_core\retry.py", line 191, in retry_target
return target()
^^^^^^^^
File "C:\GitHub\onecloud-chatbot\onecloud-chatbot\Lib\site-packages\google\api_core\grpc_helpers.py", line 74, in error_remapped_callable
raise exceptions.from_grpc_error(exc) from exc
google.api_core.exceptions.InvalidArgument: 400 Request contains an invalid argument.
|
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| 1,875,540,445 |
PR_kwDOIPDwls5ZPTgp
| 10,049 |
chat integration [wip]
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[] | null | 1 | 2023-08-31T13:31:15 | 2023-08-31T15:04:04 | 2023-08-31T14:36:39 |
CONTRIBUTOR
| null |
TODO:
* fix problem with getter
* correct output from _generate
* handle batch requests
* try to add stopping criteria
* handle ChatMessage, AIMessageChunk(?)
|
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| 1,875,502,431 |
PR_kwDOIPDwls5ZPLNr
| 10,048 |
updated prompt name in documentation for sequential chain
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[] | null | 2 | 2023-08-31T13:10:28 | 2023-08-31T23:05:24 | 2023-08-31T23:05:18 |
CONTRIBUTOR
| null |
Description: updated the prompt name in a sequential chain example so that it is not overwritten by the same prompt name in the next chain (this is a sequential chain example)
Issue: n/a
Dependencies: none
Tag maintainer: not known
Twitter handle: not on twitter, feel free to use my git username for anything
|
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PR_kwDOIPDwls5ZPIb7
| 10,047 |
Add sqlite-vss as a vector database
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[] | null | 6 | 2023-08-31T13:03:12 | 2023-09-01T18:21:45 | 2023-09-01T15:36:34 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
This adds sqlite-vss as an option for a vector database. Contains the code and a few tests. Tests are passing and the library sqlite-vss is added as optional as explained in the contributing guidelines. I adjusted the code for lint/black/ and mypy. It looks that everything is currently passing.
Adding sqlite-vss was mentioned in this issue: https://github.com/langchain-ai/langchain/issues/1019.
Also mentioned here in the sqlite-vss repo for the curious: https://github.com/asg017/sqlite-vss/issues/66
Maintainer tag: @baskaryan
|
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