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https://api.github.com/repos/langchain-ai/langchain/issues/9371
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PR_kwDOIPDwls5YIcXa
| 9,371 |
Use a submodule for pydantic v1 compat
|
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[] | null | 1 | 2023-08-17T08:27:06 | 2023-08-17T15:35:50 | 2023-08-17T15:35:49 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- Issue: the issue # it fixes (if applicable),
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|
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| 1,854,466,773 |
PR_kwDOIPDwls5YIRpE
| 9,370 |
feat(llms): support ERNIE Embedding-V1
|
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closed
| false | null |
[] | null | 5 | 2023-08-17T07:53:33 | 2023-08-21T14:52:26 | 2023-08-21T14:52:25 |
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:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
- Description: support [ERNIE Embedding-V1](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/alj562vvu), which is part of ERNIE ecology
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
|
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| 1,854,462,216 |
I_kwDOIPDwls5uiNkI
| 9,369 |
Add {language} in all template
|
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closed
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[] | null | 2 | 2023-08-17T07:50:21 | 2023-11-24T09:06:07 | 2023-11-23T16:05:44 |
CONTRIBUTOR
| null |
### Feature request
Actually, all prompt are in english. To generate a summary or answer a question in another language, all the templates need to be modified.
The modification can be :
```
prompt = PromptTemplate(
template=re.sub(
"CONCISE SUMMARY:",
"CONCISE SUMMARY IN {language}:",
summarize.stuff_prompt.prompt_template,
),
input_variables=["text", "language"],
)
```
The default "language" must be english.
### Motivation
It would be nice for all non-English speakers around the world to be able to simply appreciate the language. By default, English can be used.
### Your contribution
No contribution at this time.
|
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| 1,854,414,691 |
I_kwDOIPDwls5uiB9j
| 9,368 |
SelfQueryRetriever gives error for some queries
|
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open
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[] | null | 6 | 2023-08-17T07:18:59 | 2024-01-01T18:46:58 | null |
NONE
| null |
### System Info
Langchain Version: 0.0.245
model: vicuna-13b-v1.5
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
metadata_field_info = [
AttributeInfo(
name='lesson',
description="Lesson Number of Book",
type="integer",
)
]
document_content_description = "English Books"
retriever = SelfQueryRetriever.from_llm(
llm, db, document_content_description, metadata_field_info, verbose=True
)
# llm_chain.predict(context = context, question=question)
qa = RetrievalQA.from_chain_type(llm=llm ,chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs={"prompt": PROMPT})
res = qa(query)
```
this is one of the documents
```
File "/home/roger/Documents/GitHub/RGRgithub/roger/testllm/main.py", line 122, in qanda
res = qa(query)
^^^^^^^^^
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/chains/base.py", line 258, in __call__
raise e
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/chains/base.py", line 252, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/chains/retrieval_qa/base.py", line 130, in _call
docs = self._get_docs(question, run_manager=_run_manager)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/chains/retrieval_qa/base.py", line 210, in _get_docs
return self.retriever.get_relevant_documents(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/schema/retriever.py", line 193, in get_relevant_documents
raise e
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/schema/retriever.py", line 186, in get_relevant_documents
result = self._get_relevant_documents(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py", line 100, in _get_relevant_documents
self.llm_chain.predict_and_parse(
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/chains/llm.py", line 282, in predict_and_parse
return self.prompt.output_parser.parse(result)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/roger/Documents/GitHub/RGRgithub/roger/testgenv/lib/python3.11/site-packages/langchain/chains/query_constructor/base.py", line 52, in parse
raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Parsing text
```json
{
"query": "phoneme",
"filter": "eq(lesson, 1)"
}
raised following error:
Unexpected token Token('COMMA', ',') at line 1, column 10.
Expected one of:
* LPAR
Previous tokens: [Token('CNAME', 'lesson')]
```
### Expected behavior
- Error caused while using SelfQueryRetriever with RetrieverQA.
- showing only with some queries
- I found that in **langchain/chains/llm.py, line 282, in predict_and_parse**
```
result = self.predict(callbacks=callbacks, **kwargs)
if self.prompt.output_parser is not None:
return self.prompt.output_parser.parse(result)
else:
return result
```
- when result is, the error occurs(**_Q: what is a phoneme)_**
`'```json\n{\n "query": "phoneme",\n "filter": "eq(lesson, 1)"\n}\n```'`
- it doesn't occur when result is , **_(Q: What is a phoneme)_**
`'```json\n{\n "query": "phoneme",\n "filter": "eq(\\"lesson\\", 1)"\n}\n```'`
- I can't change the version right now.
|
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| 1,854,407,272 |
PR_kwDOIPDwls5YIExK
| 9,367 |
added support for inference from Model Garden
|
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[] | null | 6 | 2023-08-17T07:14:25 | 2023-09-01T22:58:22 | 2023-09-01T22:58:22 |
CONTRIBUTOR
| null |
#8850
|
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| 1,854,379,288 |
I_kwDOIPDwls5uh5UY
| 9,366 |
Support retry policy for ErnieBotChat
|
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[] | null | 2 | 2023-08-17T06:56:19 | 2023-10-17T00:57:52 | 2023-10-17T00:57:28 |
CONTRIBUTOR
| null |
### Feature request
Support retry policy for ErnieBotChat
### Motivation
ErnieBotChat currently not support retry policy, it will failed when reach quotas.
### Your contribution
I will submit a PR
|
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PR_kwDOIPDwls5YH44m
| 9,365 |
Fix typos in eval_chain.py
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[] | null | 1 | 2023-08-17T06:36:54 | 2023-08-17T09:01:31 | 2023-08-17T08:53:47 |
CONTRIBUTOR
| null |
Fixed two minor typos.
|
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I_kwDOIPDwls5uhqmc
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Help: How to stop the chain if no chunks are retrieved?
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[] | null | 2 | 2023-08-17T06:13:09 | 2023-11-23T16:05:50 | 2023-11-23T16:05:49 |
NONE
| null |
### Issue you'd like to raise.
I am using ConversationalRetrievalChain for RAG based chatbot and I am using custom retriever to get the relevant chunks.
```
custom_retriever = FilteredRetriever(
vectorstore=vectorstore.as_retriever(search_kwargs={"k": 5, "filter": {}}, search_type="mmr"),
category=sess_metadata["category"],
product=sess_metadata["product"],
service=sess_metadata["service"],
)
qa_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=custom_retriever,
chain_type="stuff",
combine_docs_chain_kwargs=QA_PROMPT,
return_source_documents=True,
verbose=True,
)
result = qa_chain({"question": translated_query, "chat_history": chat_history})
```
If no chunks are retrieved then I don't want the LLM to execute and just return a simple response to the customer.
How can I do that?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5uhnO1
| 9,363 |
DOC: Where i can find Elasticsearch integration docs?
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[] | null | 3 | 2023-08-17T05:59:17 | 2023-11-20T00:22:11 | 2023-11-20T00:22:02 |
CONTRIBUTOR
| null |
### Issue with current documentation:
`/usr/local/lib/python3.9/dist-packages/langchain/vectorstores/elastic_vector_search.py:135: UserWarning: ElasticVectorSearch will be removed in a future release. See Elasticsearch integration docs on how to upgrade.`
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5YHtCD
| 9,362 |
Corrected Typo in MultiPromptChain Example in router.ipynb
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[] | null | 1 | 2023-08-17T05:52:12 | 2023-08-17T08:54:44 | 2023-08-17T08:54:44 |
CONTRIBUTOR
| null |
Refined the example in router.ipynb by addressing a minor typographical error. The typo "rins" has been corrected to "rains" in the code snippet that demonstrates the usage of the MultiPromptChain. This change ensures accuracy and consistency in the provided code example.
This improvement enhances the readability and correctness of the notebook, making it easier for users to understand and follow the demonstration. The commit aims to maintain the quality and accuracy of the content within the repository.
Thank you for your attention to detail, and please review the change at your convenience.
@baskaryan , @hwchase17
|
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PR_kwDOIPDwls5YHq9L
| 9,361 |
Update "Langchain" to "LangChain" in the tutorials.mdx file
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[] | null | 1 | 2023-08-17T05:41:11 | 2023-08-17T08:54:58 | 2023-08-17T08:54:58 |
CONTRIBUTOR
| null |
In this commit, I have made a modification to the term "Langchain" to correctly reflect the project's name as "LangChain". This change ensures consistency and accuracy throughout the codebase and documentation.
@baskaryan , @hwchase17
|
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Fix Typo in "community.md"
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[] | null | 1 | 2023-08-17T05:35:10 | 2023-08-17T08:54:14 | 2023-08-17T08:54:14 |
CONTRIBUTOR
| null |
Corrected a typographical error in the "community.md" file by removing an extra word from the sentence.
@baskaryan , @hwchase17
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PR_kwDOIPDwls5YHlas
| 9,359 |
Fixed Grammatical errors in tutorials.mdx
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[] | null | 1 | 2023-08-17T05:11:30 | 2023-08-17T08:55:11 | 2023-08-17T08:55:11 |
CONTRIBUTOR
| null |
I want to convey my deep appreciation to the creator for their expert craftsmanship in developing this exceptional application. 👏 The remarkable dedication to upholding impeccable grammar and spelling in the documentation significantly enhances the polished and seamless experience for readers.
I want to stress that your feedback is invaluable to us and is genuinely cherished.
With gratitude,
@baskaryan, @hwchase17
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5YHgVQ
| 9,358 |
Corrected Grammatical errors in tutorials.mdx
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[] | null | 1 | 2023-08-17T04:44:49 | 2023-08-17T08:55:21 | 2023-08-17T08:55:21 |
CONTRIBUTOR
| null |
I want to extend my heartfelt gratitude to the creator for masterfully crafting this remarkable application. 🙌 I am truly impressed by the meticulous attention to grammar and spelling in the documentation, which undoubtedly contributes to a polished and seamless reader experience.
As always, your feedback holds immense value and is greatly appreciated.
@baskaryan , @hwchase17
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I_kwDOIPDwls5uhWCM
| 9,357 |
why the vector database of vectorstores must load document ？
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[] | null | 3 | 2023-08-17T04:38:27 | 2023-08-18T02:37:15 | 2023-08-18T02:37:15 |
NONE
| null |
### Issue with current documentation:
code
-------------
raw_documents = TextLoader('../../../state_of_the_union.txt').load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
db = Chroma.from_documents(documents, OpenAIEmbeddings())
query = "What did the president say about Ketanji Brown Jackson"
docs = await db.asimilarity_search(query)
--------------
"chroma" requires the input of a document when used. If I have existing data in my vector library and only need to retrieve data without uploading new ones, I cannot define a vectorDB.
myflow
query -----> embedding -----> vectorDB ----> llm -----> out
the vectorDB existing data, don't nedd load document
how abount use vectorstores ?
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5YHW6L
| 9,356 |
unpin pydantic
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PR_kwDOIPDwls5YHNhg
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Adding missing types in some pydantic models
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COLLABORATOR
| null |
* Adding missing types in some pydantic models -- this change is required for making the code work with pydantic v2.
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Patch to pass attribute_filter to kendra query call with get_relevant_documents kendra retriever method
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[] | null | 4 | 2023-08-17T02:12:03 | 2023-09-04T03:58:12 | 2023-09-04T03:58:12 |
NONE
| null |
**Description:**
- Add attribute_filter as an optional input argument to get_relevant_documents method of kendra retriever class. Currently it is only possible to define an attribute_filter, used to filter kendra searched docs by metadata attributes (such as s3 prefix), when creating a Kendra retriever instance.
- This patch to add attribute_filter as an optional argument to get_relevant_documents would enable programmatic changes in query filters to more easily support map reduce design patterns.
- This change simply passes the optional attribute_filter dict as an optional argument to the _kendra_query method.
- It does not store the attribute_filter as a class attribute as is the case for an attribute_filter defined when creating Kendra retriever class instance.
- If a class attribute_filter instance exists, the attribute_filter provided during get_relevant_documents supersedes it.
**Tests:**
- This is a very minor, small change. Nonetheless I still ran `make docker_tests`.
- The only kendra relevant output was below and it does not pertain to the changes made:
- `langchain/retrievers/kendra.py:24
/app/langchain/retrievers/kendra.py:24: DeprecationWarning: invalid escape sequence '\s'
res = re.sub("\s+", " ", excerpt).replace("...", "")`
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| 1,854,116,866 |
PR_kwDOIPDwls5YHG3x
| 9,353 |
Update default index type and metric type for MyScale vector store
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[] | null | 3 | 2023-08-17T01:56:33 | 2023-09-04T06:38:57 | 2023-08-24T01:26:29 |
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.
-->
We update the default index type from `IVFFLAT` to `MSTG`, a new vector type developed by MyScale.
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| 1,854,106,510 |
I_kwDOIPDwls5ug2uO
| 9,352 |
Error when trying to use ConversationBufferMemory with LLMChain
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[] | null | 1 | 2023-08-17T01:40:57 | 2023-08-17T23:00:44 | 2023-08-17T23:00:43 |
NONE
| null |
### System Info
Python 3.10
LangChain v0.0.266
### Who can help?
@eyurtsev
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the attached python test file
[test.py.zip](https://github.com/langchain-ai/langchain/files/12364594/test.py.zip)
Error observed:
[LCError.txt](https://github.com/langchain-ai/langchain/files/12364595/LCError.txt)
My python virtual environment is set with the following libraries:
openai==0.27.2
python-dotenv
tiktoken==0.3.1
langchain==0.0.266
### Expected behavior
Complete program without errors
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| 1,854,101,112 |
I_kwDOIPDwls5ug1Z4
| 9,351 |
CSV Agent Issue
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[] | null | 2 | 2023-08-17T01:32:18 | 2023-11-23T16:05:55 | 2023-11-23T16:05:54 |
NONE
| null |
### System Info
This code is exactly as in the documentation.
```
import os
from dotenv import load_dotenv
from langchain.agents import create_csv_agent
from langchain.llms import OpenAI
load_dotenv()
agent = create_csv_agent(OpenAI(temperature=0),
'train.csv',
verbose=True)
```
However, I receive this error. I have pydantic 2 installed as this is the latest version. I tried with pydantic v1 and it still doesnt work.
File "/Users/user/Desktop/coding/xero-python-oauth2-starter/venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 13, in <module>
from pydantic_v1 import BaseModel, root_validator
ModuleNotFoundError: No module named 'pydantic_v1'
I'm not sure if this is a me issue, but I've tried the common sense methods of checking.
### 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
Code is provided.
### Expected behavior
I should be able to use the csv agent.
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I_kwDOIPDwls5ug1TO
| 9,350 |
Use custom dictionary for retriever
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[] | null | 2 | 2023-08-17T01:31:41 | 2023-11-23T16:06:00 | 2023-11-23T16:05:59 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Is it possible to use a customized dictionary for langchain retriever to search for document?
For example, there is document talking about "circuit". In my organization, people use special keyword "A" which means "circuit". However, this "A" == "circuit" is not a common relationship in embedding system.
Is it possible to let langchain retriever knows this "A" == "circuit" relationship, so it can search the document talking about "circuit" without fine-tune the embedding model?
### Suggestion:
_No response_
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| 1,854,094,254 |
PR_kwDOIPDwls5YHCUs
| 9,349 |
:hourglass_flowing_sand: docstrings: `vectorstores` consistency
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| false | null |
[] | null | 3 | 2023-08-17T01:21:55 | 2023-08-24T15:02:33 | 2023-08-24T06:17:05 |
COLLABORATOR
| null |
:hourglass_flowing_sand:
- updated the top-level descriptions to a consistent format;
- changed several `ValueError` to `ImportError` in the import cases;
- changed the format of several internal functions from "name" to "_name". So, these functions are not shown in the Top-level API Reference page (with lists of classes/functions)
@baskaryan BTW Are we going to try [this OpenAI content moderation](https://openai.com/blog/using-gpt-4-for-content-moderation) for our docs?
|
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| 1,854,086,717 |
PR_kwDOIPDwls5YHAxM
| 9,348 |
(RFC) pydantic bridge
|
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[] | null | 2 | 2023-08-17T01:11:18 | 2023-09-04T03:59:13 | 2023-09-04T03:59:13 |
COLLABORATOR
| null |
specifically, allows us to start using pydantic v2 internally
|
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I_kwDOIPDwls5ugt7c
| 9,347 |
Add exclude_output_keys to VectorStoreRetrieverMemory
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[] | null | 1 | 2023-08-17T00:43:51 | 2023-11-23T16:06:05 | 2023-11-23T16:06:04 |
NONE
| null |
### Feature request
Add the parameter exclude_output_keys to VectorStoreRetrieverMemory and exclude output keys in method _form_documents similar to the case of input keys. This would enable the use of VectorStoreRetrieverMemory as read-only memory.
https://github.com/langchain-ai/langchain/blob/2e8733cf54d3cd24cf6927e4d52a212ead88be8e/libs/langchain/langchain/memory/vectorstore.py
### Motivation
I am using the ConversationChain to create a ChatBot that impersonates fictional characters based on extensive information that does not fit in the limited context size available. Therefore, I want to store the character information and the chat history from multiple sessions in two Chroma databases. While the ConversationChain populates the chat history database, the character information should be read-only.
Using exclude_input_keys resulted in only the LLM outputs being saved in the character information database. Here, it would be beneficial to be able to exclude the output_key from being stored as well.
### Your contribution
```
exclude_output_keys: Sequence[str] = Field(default_factory=tuple)
"""Output keys to exclude when constructing the document"""
```
```
def _form_documents(
self, inputs: Dict[str, Any], outputs: Dict[str, str]
) -> List[Document]:
"""Format context from this conversation to buffer."""
# Each document should only include the current turn, not the chat history
excluded_inputs = set(self.exclude_input_keys)
excluded_inputs.add(self.memory_key)
filtered_inputs = {k: v for k, v in inputs.items() if k not in excluded_inputs}
excluded_outputs = set(self.exclude_output_keys)
filtered_outputs = {k: v for k, v in outputs.items() if k not in excluded_outputs}
texts = [
f"{k}: {v}"
for k, v in list(filtered_inputs.items()) + list(filtered_outputs.items())
]
page_content = "\n".join(texts)
return [Document(page_content=page_content)]
```
I did not check if passing an empty string to Document will work or throw an error.
|
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PR_kwDOIPDwls5YG7Av
| 9,346 |
fix import v2
|
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[] | null | 1 | 2023-08-17T00:23:26 | 2023-08-17T00:33:02 | 2023-08-17T00:33:02 |
COLLABORATOR
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| 1,853,988,704 |
I_kwDOIPDwls5ugZ9g
| 9,345 |
Type error in ParentDocumentRetriever using LocalFileStore
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[] | null | 12 | 2023-08-16T22:36:06 | 2024-01-12T16:34:08 | null |
NONE
| null |
### Bug
LocalFileStore tries to treat Document as byte
```
store = LocalFileStore(get_project_relative_path("doc_store"))
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
retriever = ParentDocumentRetriever(vectorstore=vectorstore,
docstore=store,
parent_splitter=parent_splitter,
child_splitter=child_splitter)
if embed:
docs = []
data_folder = get_project_relative_path("documents")
for i, file_path in enumerate(data_folder.iterdir()):
document = TextLoader(str(file_path))
docs.extend(document.load())
retriever.add_documents(docs, None)
```
Here the broken method:
```
def mset(self, key_value_pairs: Sequence[Tuple[str, bytes]]) -> None:
"""Set the values for the given keys.
Args:
key_value_pairs: A sequence of key-value pairs.
Returns:
None
"""
for key, value in key_value_pairs:
full_path = self._get_full_path(key)
full_path.parent.mkdir(parents=True, exist_ok=True)
full_path.write_bytes(value)
```
TypeError: memoryview: a bytes-like object is required, not 'Document'
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create a LocalFileStore
Use a ParentDoucmentRetriever
### Expected behavior
Serialize the documents as bytes
|
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| 1,853,973,331 |
PR_kwDOIPDwls5YGo3m
| 9,344 |
Update agent docs, move to use-case sub-directory
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[] | null | 1 | 2023-08-16T22:17:02 | 2023-08-25T18:28:57 | 2023-08-25T18:28:56 |
COLLABORATOR
| null |
Re-structure and add new agent page
|
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PR_kwDOIPDwls5YGjb-
| 9,342 |
Cache linting venv on CI.
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[] | null | 1 | 2023-08-16T21:48:55 | 2023-08-17T15:46:59 | 2023-08-17T15:46:59 |
COLLABORATOR
| null |
Ensure that we cache the linting virtualenv as well as the pip cache for the `pip install -e langchain` step.
This is a win of about 60-90s overall.
Before:
After:
|
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| 1,853,934,808 |
I_kwDOIPDwls5ugMzY
| 9,341 |
How to fix "Token indices sequence length is longer than the specified maximum sequence length for this model"?
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[] | null | 3 | 2023-08-16T21:33:48 | 2023-12-08T16:05:30 | 2023-12-08T16:05:30 |
NONE
| null |
### Issue you'd like to raise.
I'm running llama2-13b with the below parameters, and I'm trying to summarize pdf (linked [here](https://akoustis.com/wp-content/uploads/2022/06/Single-Crystal-AlScN-on-Silicon-XBAW%E2%84%A2RF-Filter-Technology-for-Wide-Bandwidth-High-Frequency-5G-and-Wi-Fi-Applications.pdf))
```
"properties": {
"max_new_tokens": 1500,
"temperature": 0.2,
"top_p": 0.95,
"repetition_penalty": 1.15
}
```
I split the document bypages and run the load_summarize_chain. But I get an error:
```
Token indices sequence length is longer than the specified maximum sequence length for this model (13243 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1164 > 1024). Running this sequence through the model will result in indexing errors
ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (507) from primary and could not load the entire response body.
```
How do I split the pdf document or what chain/prompt should I use to summarize large pdfs and respect the max_new_tokens=1024 for llama2.
Code
```
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("Single-Crystal-AlScN-on-Silicon-XBAW™RF-Filter-Technology-for-Wide-Bandwidth-High-Frequency-5G-and-Wi-Fi-Applications.pdf")
pages = loader.load_and_split()
chain = load_summarize_chain(llm,chain_type="map_reduce")
x =chain.run(pages)
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5YGa_J
| 9,340 |
Update RAG use case (move to ntbk)
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[] | null | 1 | 2023-08-16T21:08:58 | 2023-08-25T18:27:28 | 2023-08-25T18:27:27 |
COLLABORATOR
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| 1,853,887,401 |
PR_kwDOIPDwls5YGWHS
| 9,338 |
[Fix] Update litellm to use model instead of model_name
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[] | null | 6 | 2023-08-16T20:49:44 | 2023-08-24T00:25:11 | 2023-08-24T00:25:03 |
CONTRIBUTOR
| null |
When using liteLLM allow users to set `model` using `model` instead of `model_name`. liteLLM uses `model` so using the same naming convention
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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PR_kwDOIPDwls5YGOGg
| 9,336 |
Ensure the in-project venv gets cached in CI tests.
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[] | null | 3 | 2023-08-16T20:18:40 | 2023-08-17T15:47:24 | 2023-08-17T15:47:23 |
COLLABORATOR
| null |
The previous caching configuration was attempting to cache poetry venvs created in the default shared virtualenvs directory. However, all langchain packages use `in-project = true` for their poetry virtualenv setup, which moves the venv inside the package itself instead. This meant that poetry venvs were not being cached at all.
This PR ensures that the venv gets cached by adding the in-project venv directory to the cached directories list.
It also makes sure that the cache key *only* includes the lockfile being installed, as opposed to *all lockfiles* (unnecessary cache misses) or just the *top-level lockfile* (cache hits when it shouldn't).
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PR_kwDOIPDwls5YF_QA
| 9,334 |
Test GitHub Actions caching hypothesis.
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[] | null | 1 | 2023-08-16T19:21:15 | 2023-08-16T21:34:00 | 2023-08-16T21:34:00 |
COLLABORATOR
| null |
DO NOT MERGE! Just testing a hypothesis around our GitHub Actions caching setup.
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PR_kwDOIPDwls5YF6k9
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📖 docs: `integrations/agent_toolkits`
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| false | null |
[] | null | 2 | 2023-08-16T19:03:05 | 2023-08-24T15:03:04 | 2023-08-24T06:17:47 |
COLLABORATOR
| null |
Note: There are no changes in the file names!
- The group name on the main navbar changed: `Agent toolkits` -> `Agents & Toolkits`. Examples here are the mix of the Agent and Toolkit examples because Agents and Toolkits in examples are always used together.
- Titles changed: removed "Agent" and "Toolkit" suffixes. The reason is the same.
- Formatting: mostly cleaning the header structure, so it could be better on the right-side navbar.
Main navbar is looking much cleaner now.
|
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| 1,853,736,011 |
PR_kwDOIPDwls5YF1Hw
| 9,332 |
add tiktoken integration dep
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[] | null | 1 | 2023-08-16T18:43:12 | 2023-08-16T19:09:23 | 2023-08-16T19:09:22 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5YFyOf
| 9,331 |
pydantic v2 bump -- test required changes
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[] | null | 2 | 2023-08-16T18:32:04 | 2023-09-21T21:55:52 | null |
COLLABORATOR
| null |
List of required changes after bumping langchain using bump-pydantic.
1. Decouple langsmith and langchain, copy over schema to langchain
2. Use new metaclass for pydantic v2 in tool base
3. WHEN ON PYDANTIC 2
tool base:
```
def _create_subset_model(
name: str, model: BaseModel, field_names: list
):
"""Create a pydantic model with only a subset of model's fields."""
fields = {}
for field_name in field_names:
field = model.__fields__[field_name]
fields[field_name] = (field.annotation, field)
return create_model(name, **fields) # type: ignore
```
utils
```python
def get_pydantic_field_names(pydantic_cls: Any) -> Set[str]:
"""Get field names, including aliases, for a pydantic class.
Args:
pydantic_cls: Pydantic class."""
all_required_field_names = set()
for name, field in pydantic_cls.__fields__.items():
all_required_field_names.add(name)
if field.alias:
all_required_field_names.add(field.alias)
return all_required_field_names
```
6. Add missing type annotations to pydantic objects (as these raise exceptions)
7. model_validator -> root_validator(skip_on_failure=True)
8. find -type f -name "*.py" -exec sed -i "s/@root_validator()/@root_validator(skip_on_failure=True)/g" {} +
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I_kwDOIPDwls5ufYBw
| 9,330 |
Issue: Search query based on the geoLocation using GoogleSearchAPIWrapper or SerpAPIWrapper
|
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[] | null | 4 | 2023-08-16T18:28:37 | 2023-11-23T16:06:10 | 2023-11-23T16:06:09 |
NONE
| null |
### Issue you'd like to raise.
Hi, I want to do the query based on my location. For example, what is the restaurant near me? I have Python code using SerpAPIWrapper and GoogleSearchAPIWrapper but none of the Api works based on location. below is my snippet code.
GoogleSearchAPIWrapper:
`
latitude = 37.360334
longitude = -121.994694
query = "Restaurant near me?"
search = GoogleSearchAPIWrapper(k=1) # k parameter to set the number of results
tool = Tool(
name="Google Search",
description="Search Google for recent results.",
func=search.run,
)
answer = tool.run(query)
print("answer:", answer)`
SerpAPIWrapper:
`
latitude =37.360334# 37.379279 #37.360334
longitude = -121.994694 #-121.960661 #-121.994694
params = {
"google_domain":"google.com",
# "engine": "google", # Set parameter to google to use the Google API engine.
"type": "search",
"engine":"google_maps",
"gl": "us",
"hl": "en",
"ll" : "@{},{},14z".format(latitude,longitude)
}
search = SerpAPIWrapper(params=params)
tools = [
Tool(
name = "search",
func=search.run,
description="Useful for when you need to answer questions about anything to google search"
),
]
# require argument : expects to be used with a memory component.
memory = ConversationBufferMemory(memory_key="chat_history")
llm=OpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
agent_chain.run("resturant near me ?")
`
I want my search based on my location using latitude and longitude. Thanks in advance!
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5YFj77
| 9,328 |
Third branch
|
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[] | null | 1 | 2023-08-16T17:38:44 | 2023-08-16T17:39:51 | 2023-08-16T17:38:58 |
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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| 1,853,549,729 |
PR_kwDOIPDwls5YFMqv
| 9,327 |
Pydantic Compatibility
|
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[] | null | 1 | 2023-08-16T16:16:21 | 2023-08-16T22:55:54 | 2023-08-16T22:55:53 |
COLLABORATOR
| null |
Pydantic Compatibility Guidelines for migration plan + debugging
|
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I_kwDOIPDwls5uefiw
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How to utilize the vector store for direct text while using Chat Models?
|
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[] | null | 2 | 2023-08-16T15:37:35 | 2023-11-22T16:05:59 | 2023-11-22T16:05:58 |
NONE
| null |
### Issue with current documentation:
I'm attempting to use Meilisearch as a vector store with Chat Models, however I'm a little confused about how to use the following code to send the input from Meilisearch to Chat Models. Below is the code which you gave
```
from langchain.vectorstores import Meilisearch
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.documents import Document
from langchain.document_loaders.pdf import PyPDFLoader
import meilisearch
# Assuming you have a list of PDF files
pdf_files = ["file1.pdf", "file2.pdf", ...]
# Create a PDF loader
pdf_loader = PyPDFLoader()
# Load the documents from the PDF files
pdf_documents = [pdf_loader.load(file) for file in pdf_files]
# Create a Meilisearch client
client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
embeddings = OpenAIEmbeddings()
vectorstore = Meilisearch(
embedding=embeddings,
client=client,
index_name='langchain_demo',
text_key='text')
# Extract the texts from the documents
texts = [doc.page_content for doc in pdf_documents]
# Add the texts to the vector store
vectorstore.add_texts(texts)
```
Below is the code which i wrote initially which is not utilizing the vector store
```
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from dotenv import load_dotenv
from pytesseract import image_to_string
from langchain.text_splitter import RecursiveCharacterTextSplitter, Document
from PIL import Image
from io import BytesIO
import pypdfium2 as pdfium
import pandas as pd
import pytesseract
import json
import os
import requests
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
load_dotenv()
os.environ["OPENAI_API_KEY"] = "sk-H......."
class DocumentProcessor:
def __init__(self):
self.llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
def convert_pdf_to_images(self, file_path, scale=300 / 72):
pdf_file = pdfium.PdfDocument(file_path)
page_indices = [i for i in range(len(pdf_file))]
renderer = pdf_file.render(
pdfium.PdfBitmap.to_pil,
page_indices=page_indices,
scale=scale,
)
final_images = []
for i, image in zip(page_indices, renderer):
image_byte_array = BytesIO()
image.save(image_byte_array, format='jpeg', optimize=True)
image_byte_array = image_byte_array.getvalue()
final_images.append(dict({i: image_byte_array}))
return final_images
def extract_text_from_img(self, list_dict_final_images):
image_list = [list(data.values())[0] for data in list_dict_final_images]
image_content = []
for index, image_bytes in enumerate(image_list):
image = Image.open(BytesIO(image_bytes))
raw_text = str(image_to_string(image))
image_content.append(raw_text)
return "\\n".join(image_content)
def extract_content_from_url(self, url):
images_list = self.convert_pdf_to_images(url)
text_with_pytesseract = self.extract_text_from_img(images_list)
return text_with_pytesseract
def extract_structured_data(self, content):
template = """
You are an expert admin people who will extract core information from documents
{content}
Above is the content; please try to return the abstract and extractive summary
"""
prompt = PromptTemplate(
input_variables=["content"],
template=template,
)
chain = LLMChain(llm=self.llm, prompt=prompt)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1024, # Maximum size of chunks to return
chunk_overlap=50, # Overlap in tokens between chunks
)
# Create a Document object for each document
documents = [Document(page_content=text) for text in content]
# Split the documents into chunks
chunks = text_splitter.split_documents(documents)
results = [chain.run(content=chunk) for chunk in chunks]
print(results)
return results
def process_documents(self, file_paths):
results = []
for file_path in file_paths:
content = self.extract_content_from_url(file_path)
data = self.extract_structured_data(content)
if isinstance(data, list):
results.extend(data)
else:
results.append(data)
return results
# Example usage
if __name__ == '__main__':
document_processor = DocumentProcessor()
uploaded_files_paths = [r'Questionnaire_2021 (1).pdf']
processed_results = document_processor.process_documents(uploaded_files_paths)
if len(processed_results) > 0:
try:
df = pd.DataFrame(processed_results)
print("Results:")
df.to_excel('output.xlsx', index=False)
print(df)
except Exception as e:
print(f"An error occurred while creating the DataFrame: {e}")
```
Can you please try to update the above code which I wrote with the code which you gave i.e. the code which has the vector store?
### Idea or request for content:
_No response_
|
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| 1,853,458,017 |
I_kwDOIPDwls5ueYZh
| 9,325 |
pgvector support for max_marginal_relevance_search
|
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[] | null | 1 | 2023-08-16T15:20:33 | 2023-09-17T21:38:32 | 2023-09-17T21:38:32 |
NONE
| null |
### Feature request
Add support for `max_marginal_relevance_search` to `pgvector` vector stores
### Motivation
Would like to be able to do `max_marginal_relevance_search` over `pgvector` vector stores
### Your contribution
N/A
|
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| 1,853,439,627 |
I_kwDOIPDwls5ueT6L
| 9,324 |
Is there any option to store direct text to VectorDB to get faster response?
|
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[] | null | 9 | 2023-08-16T15:09:40 | 2023-12-19T00:49:13 | 2023-12-19T00:49:12 |
NONE
| null |
### Issue with current documentation:
Instead of embeddings, I'm using chat models with RecursiveTextSplitter because they simply return the response without any further justification. The input PDF files that I'm providing have a 36k input length. It takes a long time to return the output when using straight code. Can we store the direct text in any vector database and use it to get faster response?
Note: I'm not talking about embeddings, i'm trying to use direct chat models from OpenAI
Below is the code which i'm using
```
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from dotenv import load_dotenv
from pytesseract import image_to_string
from langchain.text_splitter import RecursiveCharacterTextSplitter, Document
from PIL import Image
from io import BytesIO
import pypdfium2 as pdfium
import pandas as pd
import pytesseract
import json
import os
import requests
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
load_dotenv()
os.environ["OPENAI_API_KEY"] = "sk-H......."
class DocumentProcessor:
def __init__(self):
self.llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
def convert_pdf_to_images(self, file_path, scale=300 / 72):
pdf_file = pdfium.PdfDocument(file_path)
page_indices = [i for i in range(len(pdf_file))]
renderer = pdf_file.render(
pdfium.PdfBitmap.to_pil,
page_indices=page_indices,
scale=scale,
)
final_images = []
for i, image in zip(page_indices, renderer):
image_byte_array = BytesIO()
image.save(image_byte_array, format='jpeg', optimize=True)
image_byte_array = image_byte_array.getvalue()
final_images.append(dict({i: image_byte_array}))
return final_images
def extract_text_from_img(self, list_dict_final_images):
image_list = [list(data.values())[0] for data in list_dict_final_images]
image_content = []
for index, image_bytes in enumerate(image_list):
image = Image.open(BytesIO(image_bytes))
raw_text = str(image_to_string(image))
image_content.append(raw_text)
return "\\n".join(image_content)
def extract_content_from_url(self, url):
images_list = self.convert_pdf_to_images(url)
text_with_pytesseract = self.extract_text_from_img(images_list)
return text_with_pytesseract
def extract_structured_data(self, content):
template = """
You are an expert admin people who will extract core information from documents
{content}
Above is the content; please try to return the abstract and extractive summary
"""
prompt = PromptTemplate(
input_variables=["content"],
template=template,
)
chain = LLMChain(llm=self.llm, prompt=prompt)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1024, # Maximum size of chunks to return
chunk_overlap=50, # Overlap in tokens between chunks
)
# Create a Document object for each document
documents = [Document(page_content=text) for text in content]
# Split the documents into chunks
chunks = text_splitter.split_documents(documents)
results = [chain.run(content=chunk) for chunk in chunks]
print(results)
return results
def process_documents(self, file_paths):
results = []
for file_path in file_paths:
content = self.extract_content_from_url(file_path)
data = self.extract_structured_data(content)
if isinstance(data, list):
results.extend(data)
else:
results.append(data)
return results
# Example usage
if __name__ == '__main__':
document_processor = DocumentProcessor()
uploaded_files_paths = [r'Questionnaire_2021 (1).pdf']
processed_results = document_processor.process_documents(uploaded_files_paths)
if len(processed_results) > 0:
try:
df = pd.DataFrame(processed_results)
print("Results:")
df.to_excel('output.xlsx', index=False)
print(df)
except Exception as e:
print(f"An error occurred while creating the DataFrame: {e}")
```
Can anyone please help me with this?
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5YEzOe
| 9,323 |
docs: remove multiple code blocks from comma-separated docs
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[] | null | 1 | 2023-08-16T15:05:42 | 2023-08-16T18:51:59 | 2023-08-16T18:51:58 |
CONTRIBUTOR
| null | null |
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PR_kwDOIPDwls5YEycD
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Fix typo in llm_router.py
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[] | null | 1 | 2023-08-16T15:03:39 | 2023-08-24T01:24:04 | 2023-08-16T22:56:45 |
CONTRIBUTOR
| null |
Fix typo
|
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PR_kwDOIPDwls5YEtxi
| 9,321 |
Fix API reference docs
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[] | null | 1 | 2023-08-16T14:51:59 | 2023-08-16T22:56:55 | 2023-08-16T22:56:54 |
COLLABORATOR
| null |
Do not document members nested within any private component
|
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PR_kwDOIPDwls5YEnsF
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Output of running `bump-pydantic` on `libs/langchain`.
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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:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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I_kwDOIPDwls5ueAQk
| 9,319 |
Inference parameters for Bedrock anthropic model showing problems with max_tokens_for_sample parameter
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[] | null | 7 | 2023-08-16T14:25:02 | 2023-10-22T04:44:28 | 2023-08-21T13:49:59 |
NONE
| null |
### System Info
Python 3.10.6
Langchain 0.0.220
### Who can help?
@3
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When I instantiate the model and chain like this:
```
llm = Bedrock(model_id="anthropic.claude-v1",
model_kwargs={'max_tokens_to_sample': 2048,
'temperature': 1.0,
'top_k': 250,
'top_p': 0.999,
'stop_sequences': ['Human:']},
client=bedrock_client)
chain = load_qa_chain(llm, chain_type='stuff', verbose=False)
```
and proceed with `chain.run(input_documents=context, question=query)`, I am getting an error when `len(context) > 50`. However, I am passing `max_tokens_to_sample = 2048`. If `len(context) < 50` it works perfectly fine. The error I am getting is:
```
Traceback (most recent call last):
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/lambdas/processQnA/trigger.py", line 59, in <module>
main()
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/lambdas/processQnA/trigger.py", line 51, in main
response = handle_event(q, None)
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/lambdas/processQnA/handler.py", line 92, in handle_event
response = ask_question(embeddings, chain, indexes, question)
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/lambdas/processQnA/utils.py", line 225, in ask_question
return chain.run(input_documents=context, question=query)
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 293, in run
return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 166, in __call__
raise e
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 160, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py", line 84, in _call
output, extra_return_dict = self.combine_docs(
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 87, in combine_docs
return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/llm.py", line 252, in predict
return self(kwargs, callbacks=callbacks)[self.output_key]
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 166, in __call__
raise e
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 160, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/llm.py", line 92, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/chains/llm.py", line 102, in generate
return self.llm.generate_prompt(
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/llms/base.py", line 141, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/llms/base.py", line 227, in generate
output = self._generate_helper(
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/llms/base.py", line 178, in _generate_helper
raise e
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/llms/base.py", line 165, in _generate_helper
self._generate(
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/llms/base.py", line 525, in _generate
self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
File "/home/jlopezcolmenarejo/Dev/ai-assistant-adopcat-poc/.venv/lib/python3.10/site-packages/langchain/llms/bedrock.py", line 190, in _call
raise ValueError(f"Error raised by bedrock service: {e}")
ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: The provided inference configurations are invalid
```
I have checked that `bedrock.py/LLMInputOutputAdapter.prepare_input` does NOT run this: `input_body["max_tokens_to_sample"] = 50`. In fact, it just add the prompt. It returns something like:
`{'max_tokens_to_sample': 2048, 'temperature': 1.0, 'top_k': 250, 'top_p': 0.999, 'stop_sequences': ['Human:'], 'prompt': "Use the following pieces of context to answer the question at the end. Blah blah blah ...}`
### Expected behavior
No errors should be raised.
|
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| 1,853,328,699 |
PR_kwDOIPDwls5YEcpF
| 9,318 |
feat: add support for arxiv identifier in ArxivAPIWrapper()
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[] | null | 9 | 2023-08-16T14:08:23 | 2023-09-28T00:35:16 | 2023-09-28T00:35:16 |
CONTRIBUTOR
| null |
- Description: this PR adds the support for arxiv identifier of the ArxivAPIWrapper. I modified the `run()` and `load()` functions in `arxiv.py`, using regex to recognize if the query is in the form of arxiv identifier (see [https://info.arxiv.org/help/find/index.html](https://info.arxiv.org/help/find/index.html)). If so, it will directly search the paper corresponding to the arxiv identifier. I also modified and added tests in `test_arxiv.py`.
- Issue: #9047
- Dependencies: N/A
- Tag maintainer: N/A
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| 9,317 |
Add bucket and object key to metadata in S3 loader
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CONTRIBUTOR
| null |
- Description: this PR adds `s3_object_key` and `s3_bucket` to the doc metadata when loading an S3 file. This is particularly useful when using `S3DirectoryLoader` to remove the files from the dir once they have been processed (getting the object keys from the metadata `source` field seems brittle)
- Dependencies: N/A
- Tag maintainer: ?
- Twitter handle: _cbornet
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I_kwDOIPDwls5udzLF
| 9,316 |
How to use RecursiveTextSplitter for Chat Models like OpenAI and LLama?
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[] | null | 6 | 2023-08-16T13:56:22 | 2023-11-26T16:06:54 | 2023-11-26T16:06:53 |
NONE
| null |
### Issue with current documentation:
I developed a piece of code that will read data from a pdf file, send it to chat models, and then return the result. What to do if the input length is too long when using chat models? I attempted embeddings, but the solutions are simple and not as good as Chat models, in my opinion. For chat models, I tried using RecursiveTextSplitter, but it didn't work. The code is below.
```
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from dotenv import load_dotenv
from pytesseract import image_to_string
from langchain.text_splitter import RecursiveCharacterTextSplitter
from PIL import Image
from io import BytesIO
import pypdfium2 as pdfium
import pandas as pd
import pytesseract
import json
import os
import requests
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
load_dotenv()
os.environ["OPENAI_API_KEY"] = "sk-H................."
class DocumentProcessor:
def __init__(self):
self.llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
def convert_pdf_to_images(self, file_path, scale=300 / 72):
pdf_file = pdfium.PdfDocument(file_path)
page_indices = [i for i in range(len(pdf_file))]
renderer = pdf_file.render(
pdfium.PdfBitmap.to_pil,
page_indices=page_indices,
scale=scale,
)
final_images = []
for i, image in zip(page_indices, renderer):
image_byte_array = BytesIO()
image.save(image_byte_array, format='jpeg', optimize=True)
image_byte_array = image_byte_array.getvalue()
final_images.append(dict({i: image_byte_array}))
return final_images
def extract_text_from_img(self, list_dict_final_images):
image_list = [list(data.values())[0] for data in list_dict_final_images]
image_content = []
for index, image_bytes in enumerate(image_list):
image = Image.open(BytesIO(image_bytes))
raw_text = str(image_to_string(image))
image_content.append(raw_text)
return "\\n".join(image_content)
def extract_content_from_url(self, url):
images_list = self.convert_pdf_to_images(url)
text_with_pytesseract = self.extract_text_from_img(images_list)
return text_with_pytesseract
def extract_structured_data(self, content):
template = """
You are an expert admin people who will extract core information from documents
{content}
Above is the content; please try to return the abstract and extractive summary
"""
prompt = PromptTemplate(
input_variables=["content"],
template=template,
)
chain = LLMChain(llm=self.llm, prompt=prompt)
# results = chain.run(content=content, data_points=self.data_points)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(content)
results = [chain.run(content=chunk) for chunk in chunks]
print(results)
return results
def process_documents(self, file_paths):
results = []
for file_path in file_paths:
content = self.extract_content_from_url(file_path)
data = self.extract_structured_data(content)
if isinstance(data, list):
results.extend(data)
else:
results.append(data)
return results
# Example usage
if __name__ == '__main__':
document_processor = DocumentProcessor()
uploaded_files_paths = [r'Questionnaire_2021 (1).pdf']
processed_results = document_processor.process_documents(uploaded_files_paths)
if len(processed_results) > 0:
try:
df = pd.DataFrame(processed_results)
print("Results:")
df.to_excel('output.xlsx', index=False)
print(df)
except Exception as e:
print(f"An error occurred while creating the DataFrame: {e}")
```
Below is the error for above code
```
Traceback (most recent call last):
File "Documents\langchain_projects\Invoice Extraction using LLM\testing.py", line 117, in <module>
processed_results = document_processor.process_documents(uploaded_files_paths)
File "Documents\langchain_projects\Invoice Extraction using LLM\testing.py", line 104, in process_documents
data = self.extract_structured_data(content)
File "Documents\langchain_projects\Invoice Extraction using LLM\testing.py", line 95, in extract_structured_data
chunks = text_splitter.split_documents(content)
File "anaconda3\lib\site-packages\langchain\text_splitter.py", line 112, in split_documents
texts.append(doc.page_content)
AttributeError: 'str' object has no attribute 'page_content'
```
Below is the error if i'm not using RecursiveTextSplitter
`openai.error.InvalidRequestError: This model's maximum context length is 4097 tokens. However, your messages resulted in 36248 tokens. Please reduce the length of the messages.`
Could someone possibly explain to me how to apply RecursiveTextSplitter to chat models rather than embeddings?
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5YEWy9
| 9,315 |
Fix issue with openai-function-agent ai message parsing
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[] | null | 9 | 2023-08-16T13:54:27 | 2023-08-25T08:59:57 | 2023-08-23T00:06:28 |
CONTRIBUTOR
| null |
- Description: Fixes an issue when the function call arguments are not valid JSON
- Issue: #9307
- Tag maintainer: @hwchase17 @eyurtsev
- Twitter handle: @pelaseyed
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| 1,853,259,088 |
PR_kwDOIPDwls5YENZO
| 9,314 |
Airbyte loaders: Fix last_state getter
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[] | null | 1 | 2023-08-16T13:33:38 | 2023-08-16T22:56:34 | 2023-08-16T22:56:33 |
CONTRIBUTOR
| null |
This PR fixes the Airbyte loaders when doing incremental syncs. The notebooks are calling out to access `loader.last_state` to get the current state of incremental syncs, but this didn't work due to a refactoring of how the loaders are structured internally in the original PR.
This PR fixes the issue by adding a `last_state` property that forwards the state correctly from the CDK adapter.
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PR_kwDOIPDwls5YEFi8
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Fix typo in huggingface_textgen_inference.ipynb
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[] | null | 3 | 2023-08-16T13:14:42 | 2023-08-19T16:49:06 | 2023-08-16T23:22:21 |
CONTRIBUTOR
| null |
Replaced incorrect `stream` parameter by `streaming` on Integrations docs.
|
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| 1,853,216,544 |
I_kwDOIPDwls5uddcg
| 9,312 |
'delete' function is not implemented in PGVector
|
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[] | null | 3 | 2023-08-16T13:11:26 | 2023-11-22T16:06:09 | 2023-11-22T16:06:08 |
NONE
| null |
### System Info
langchain version 0.0.266, using python3
### Who can help?
@eyurtsev
@ago
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.embeddings import VertexAIEmbeddings
from langchain.vectorstores import PGVector
embeddings = VertexAIEmbeddings()
vectorstore = PGVector(
collection_name=<collection_name>,
connection_string=<connection_string>,
embedding_function=embeddings,
)
vectorstore.delete(ids=[<some_id>])
### Expected behavior
the vector will be deleted, no error will be arosed
|
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PR_kwDOIPDwls5YD9Qx
| 9,311 |
Fix variable case in code snippet in docs
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[] | null | 2 | 2023-08-16T12:53:31 | 2023-08-16T20:34:47 | 2023-08-16T20:34:47 |
CONTRIBUTOR
| null |
- Description: Fix a minor variable naming inconsistency in a code snippet in the docs
- Issue: N/A
- Dependencies: none
- Tag maintainer: N/A
- Twitter handle: N/A
|
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| 1,853,181,280 |
I_kwDOIPDwls5udU1g
| 9,310 |
Use original_question if SelfQueryRetriever if the filter is empty
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[] | null | 5 | 2023-08-16T12:51:20 | 2023-12-19T08:21:38 | null |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.266
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
In the SelfQueryRetriever, the code try to generate a filter, and simplifies the question.
But, if the first step can not generate que specific filter, the simplified version of the question is used. This changes the answer completely.
It's possible to use `use_original_query` to force the usage of the original query.
But, I think if it's possible to generate a filter, it's a good idea to use the simplified version of the question.
Else, it's necessary to use the original question, because the filter is no longer involved.
Ask a question with a filter, but the filter is not associated with the `metadata_field_info`.
The calculated filter is empty and the simplified version is used.
### Expected behavior
I think if it's possible to generate a filter, it's a good idea to use the simplified version of the question.
Else, it's *necessary* to use the original question, because the filter is no longer involved.
|
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PR_kwDOIPDwls5YD8XI
| 9,309 |
Use the default query if the generated filter is empty.
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[] | null | 3 | 2023-08-16T12:51:11 | 2023-09-04T04:05:20 | 2023-09-04T04:05:20 |
CONTRIBUTOR
| null |
In the SelfQueryRetriever, the code try to generate a filter, and simplifies the question.
But, if the first step can not generate que specific filter, the simplified version of the question is used. This changes the answer completely.
It's possible to use `use_original_query` to force the usage of the original query.
But, I think if it's possible to generate a filter, it's a good idea to use the simplified version of the question.
Else, it's necessary to use the original question, because the filter is no longer involved.
|
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| 1,853,173,863 |
I_kwDOIPDwls5udTBn
| 9,308 |
Huggingface TextGen Inference streaming example not working
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[] | null | 2 | 2023-08-16T12:47:24 | 2023-08-16T13:15:28 | 2023-08-16T13:15:28 |
CONTRIBUTOR
| null |
### System Info
Latest Python and LangChain version.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Go to the documentation: https://python.langchain.com/docs/integrations/llms/huggingface_textgen_inference
2. Test the `Streaming` example:
```
from langchain.llms import HuggingFaceTextGenInference
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = HuggingFaceTextGenInference(
inference_server_url="http://localhost:8010/",
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.03,
stream=True
)
llm("What did foo say about bar?", callbacks=[StreamingStdOutCallbackHandler()])
```
Throws an error:
```
pydantic.error_wrappers.ValidationError: 1 validation error for HuggingFaceTextGenInference
stream
extra fields not permitted (type=value_error.extra)
```
But `streaming=True` worked. So maybe it's a typo in the documentation.
### Expected behavior
To get working the example.
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I_kwDOIPDwls5udSq3
| 9,307 |
`create_csv_agent` fails due to arguments from pandas not being valid JSON.
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| false | null |
[] | null | 2 | 2023-08-16T12:46:43 | 2023-11-22T16:06:14 | 2023-11-22T16:06:13 |
CONTRIBUTOR
| null |
### System Info
LangChain `v0.0.264`
### Who can help?
@hwchase17 @eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an agent using `create_csv_agent`.
2. Add the attached CSV
3. Run a query: `What is my MONTHLY BUDGET SUMMARY`
[Personalbudget.csv](https://github.com/langchain-ai/langchain/files/12359540/Personalbudget.csv)
Trace:
```
raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Could not parse tool input: {'name': 'python', 'arguments': "summary_row = df[df['Personal Monthly Budget'] == 'MONTHLY BUDGET SUMMARY']\nsummary_row"} because the `arguments` is not valid JSON.
```
### Expected behavior
The arguments passed to the parser should be always be valid JSON.
|
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| 1,853,080,158 |
I_kwDOIPDwls5uc8Je
| 9,305 |
RecursiveCharacterTextSplitter splits even if text is smaller than chunk size
|
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[] | null | 3 | 2023-08-16T11:51:15 | 2023-10-11T16:46:11 | 2023-10-11T16:46:11 |
NONE
| null |
### System Info
Python 3.10.12
Langchain 0.0.266
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Import any loader or just directly use the RecursiveCharacterTextSplitter
define chunk size to lets say 3950
Get a text that is way smaller, for example 1k tokens.
run the text splitter and recieve multiple documents.
```
from langchain.text_splitter import SentenceTransformersTokenTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders.youtube import YoutubeLoader
def get_token_count(text):
splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0)
text_token_count = splitter.count_tokens(text=text.replace("\n"," "))
return text_token_count
text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n", " "], chunk_size=3950, chunk_overlap=100)
def summarize_youtube_video(youtube_url: str) -> str:
"""
Useful to get the summary of a youtube video. Applies if the user sends a youtube link.
"""
id_input = youtube_url.split("=")[1]
splitter = text_splitter
loader = YoutubeLoader(id_input)
docs = loader.load_and_split(splitter)
return docs
docs = summarize_youtube_video("https://www.youtube.com/watch?v=vXIkc40UiH0")
for item in docs:
print(get_token_count(item.page_content))
```
### Expected behavior
If the text is smaller than the chunk size, only one document should get returned.
|
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PR_kwDOIPDwls5YDUOX
| 9,304 |
Add the possibility to configure boto3 in the S3 loaders
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[] | null | 4 | 2023-08-16T10:57:28 | 2023-09-04T18:00:28 | 2023-09-04T04:06:49 |
CONTRIBUTOR
| null |
- Description: this PR adds the possibility to configure boto3 in the S3 loaders. Any named argument you add will be used to create the Boto3 session. This is useful when the AWS credentials can't be passed as env variables or can't be read from the credentials file.
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: ?
- Twitter handle: cbornet_
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I_kwDOIPDwls5ucJaq
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Graph DB QA chain
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[] | null | 6 | 2023-08-16T09:37:54 | 2024-01-02T21:17:04 | 2023-12-25T16:09:09 |
NONE
| null |
### Issue you'd like to raise.
How to resolve [This model's maximum context length is 4097 tokens] ?
If Graph DB have many nodes, can not send those tokens to OpenAI by one time.
### Suggestion:
_No response_
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I_kwDOIPDwls5ucB6O
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With HuggingFaceEmbeddings, embedding of individual documents in the vectorstore can influence each other
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[] | null | 4 | 2023-08-16T09:19:03 | 2023-11-23T10:13:43 | null |
NONE
| null |
### System Info
langchain/0.0.258, Python 3.10.10
### Who can help?
@hw
@issam9
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
According the documentation/tutorials a vectorstore is created by these steps
1. Create a list of documents by some documents loaders
2. Use a text splitter for splitting the documents into smaller chunks
3. Pass the resulting list to the vectorstore
See the tutorial https://learn.deeplearning.ai/langchain-chat-with-your-data/lesson/4/vectorstores-and-embedding
In case of the HuggingFaceEmbeddings the computed embeddings of the chunks of the documents can influence each other.
This caused by the fact that the HuggingFaceEmbeddings is making a single call of the 'encode' method of the SentenceTransformer class, see https://github.com/langchain-ai/langchain/blob/1d55141c5016a2e197d3eed2844d55460d207801/libs/langchain/langchain/embeddings/huggingface.py#L91
The SentenceTransformer has a pooling layer as last layer, with 'pooling_mode_cls_token' or 'pooling_mode_mean'. And in the
'encode' method the pooling layer is not cleared during looping through the sentences, see https://github.com/UKPLab/sentence-transformers/blob/a458ce79c40fef93d5ecc66931b446ea65fdd017/sentence_transformers/SentenceTransformer.py#L159
By that the embedding of the chunks are influencing each other by the pooling layer.
You can reproduce it with the following codes snipplet:
def print_document_embeddings(splits: list[Document], document_name: str):
model_name = 'sentence-transformers/multi-qa-mpnet-base-dot-v1'
embedding = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={}, encode_kwargs={})
vectordb = Chroma.from_documents(splits, embedding)
dump = vectordb.get(include=['embeddings', 'metadatas', 'documents'])
for metadata, document, vector in zip(dump['metadatas'], dump['documents'], dump['embeddings']):
if metadata['source'] == document_name:
print(f'Embedding: {vector[:3]}...')
print(f'Document: {document}')
print('-' * 10)
vectordb.delete()
doc1 = Document(page_content='The embedding vectors of a document must not depend on any other documents.', metadata={'source': 'document 1'})
doc2 = Document(page_content='The grass is green.', metadata={'source': 'document 2'})
doc3 = Document(page_content=' Any text larger the document 1.'*10, metadata={'source': 'document 3'})
print('Vector store with [doc1,doc2]')
print_document_embeddings([doc1,doc2],'document 1')
print('Vector store with [doc1,doc3]')
print_document_embeddings([doc1, doc3],'document 1')
In the output you can see that the embeddings is not the same (the changes are small because document 3 is small, but they can be arbitrary large)
Vector store with [doc1,doc2]
Embedding: [0.03044097125530243, -0.5951688289642334, -0.11154567450284958]...
Document: The embedding vectors of a document must not depend on any other documents.
----------
Vector store with [doc1,doc3]
Embedding: [0.030441032722592354, -0.5951685905456543, -0.11154570430517197]...
Document: The embedding vectors of a document must not depend on any other documents.
### Expected behavior
The expected behavior is that the embedding of a document is always that same, it can not be influenced by any other documents in the vectorstore.
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PR_kwDOIPDwls5YCt7z
| 9,300 |
Fix SelfAskOutputParser (from langchain.agents.self_ask_with_search.output_parser)
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[] | null | 6 | 2023-08-16T09:06:15 | 2023-11-10T22:07:01 | 2023-11-10T22:07:00 |
NONE
| null |
# Description of the change
By replacing hard coded position of "follow up" and "final output" pattern with regex pattern matching using "re" I reduce failed outputs like markdown list or any multiline outputs
Example of the issue:
```
from langchain import OpenAI, SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
tools = [
Tool(
name="Intermediate Answer",
func=search.run,
description="useful for when you need to ask with search",
)
]
self_ask_with_search = initialize_agent(
tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True
)
out = self_ask_with_search.run(
"List men and women Rolland Garros winners between 2020 and 2023?"
)
-------------------------
> Entering new AgentExecutor chain...
Yes.
Follow up: Who won the men's Rolland Garros in 2020?
Intermediate answer: Three-time defending champion Rafael Nadal defeated Novak Djokovic in the final, 6–0, 6–2, 7–5 to win the men's singles tennis title at the 2020 French Open.
Follow up: Who won the women's Rolland Garros in 2020?
Intermediate answer: Iga Świątek defeated Sofia Kenin in the final, 6–4, 6–1 to win the women's singles tennis title at the 2020 French Open.
Follow up: Who won the men's Rolland Garros in 2021?
Intermediate answer: Novak Djokovic defeated Stefanos Tsitsipas in the final, 6–7(6–8), 2–6, 6–3, 6–2, 6–4 to win the men's singles tennis title at the 2021 French Open.
Follow up: Who won the women's Rolland Garros in 2021?
Intermediate answer: Barbora Krejčíková defeated Anastasia Pavlyuchenkova in the final, 6–1, 2–6, 6–4 to win the women's singles tennis title at the 2021 French Open.
Follow up: Who won the men's Rolland Garros in 2022?
Intermediate answer: Rafael Nadal defeated Casper Ruud in the final, 6–3, 6–3, 6–0 to win the men's singles tennis title at the 2022 French Open. It was his record-extending ...
Follow up: Who won the women's Rolland Garros in 2022?
Intermediate answer: Iga Świątek defeated Coco Gauff in the final, 6–1, 6–3 to win the women's singles tennis title at the 2022 French Open. It was her second French Open title, ...
Follow up: Who won the men's Rolland Garros in 2023?
Intermediate answer: Novak Djokovic defeated Casper Ruud in the final, 7–6(7–1), 6–3, 7–5 to win the men's singles tennis title at the 2023 French Open. It was his third French ...
Follow up: Who won the women's Rolland Garros in 2023?
Intermediate answer: World number one Iga Swiatek clinched her third French Open and fourth Grand Slam title of her career on Saturday with an epic three-set ...
---------------------------------------------------------------------------
OutputParserException Traceback (most recent call last)
...
File /usr/local/Caskroom/miniconda/base/envs/py_3_10/lib/python3.10/site-packages/langchain/agents/self_ask_with_search/output_parser.py:17, in SelfAskOutputParser.parse(self, text)
15 if not any([follow in last_line for follow in self.followups]):
16 if self.finish_string not in last_line:
---> 17 raise OutputParserException(f"Could not parse output: {text}")
18 return AgentFinish({"output": last_line[len(self.finish_string) :]}, text)
20 after_colon = text.split(":")[-1].strip()
OutputParserException: Could not parse output: So the final answer is:
2020: Men's - Rafael Nadal, Women's - Iga Świątek
2021: Men's - Novak Djokovic, Women's - Barbora Krejčíková
2022: Men's - Rafael Nadal, Women's - Iga Świątek
2023: Men's - Novak Djokovic, Women's - Iga Świątek
```
Using regex matching now we obtain a correct answer:
```
> Entering new AgentExecutor chain...
Yes.
Follow up: Who won the men's singles at the 2020 French Open?
Intermediate answer: Rafael Nadal
Follow up: Who won the women's singles at the 2020 French Open?
Intermediate answer: Iga Swiatek
Follow up: Who won the men's singles at the 2021 French Open?
Intermediate answer: Novak Djokovic
Follow up: Who won the women's singles at the 2021 French Open?
Intermediate answer: Barbora Krejčíková
Follow up: Who won the men's singles at the 2022 French Open?
Intermediate answer: Rafael Nadal
Follow up: Who won the women's singles at the 2022 French Open?
Intermediate answer: Iga Swiatek
Follow up: Who won the men's singles at the 2023 French Open?
Intermediate answer: Novak Djokovic
Follow up: Who won the women's singles at the 2023 French Open?
Intermediate answer: Iga Swiatek
So the final answer is:
2020: Rafael Nadal (men's singles) and Iga Swiatek (women's singles)
2021: Novak Djokovic (men's singles) and Barbora Krejčíková (women's singles)
2022: Rafael Nadal (men's singles) and Iga Swiatek (women's singles)
2023: Novak Djokovic (men's singles) and Iga Swiatek (women's singles)
> Finished chain.
```
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See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
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| 1,852,805,480 |
I_kwDOIPDwls5ub5Fo
| 9,299 |
other llm api
|
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| false | null |
[] | null | 4 | 2023-08-16T08:56:57 | 2023-11-27T16:07:27 | 2023-11-27T16:07:26 |
NONE
| null |
### Issue you'd like to raise.
Hello, I have a question, I didn't find it from the official documentation, I want to know, if I have another llm now, he provides me with a calling api, how can I do it, can it be used like openai, such as This https://python.langchain.com/docs/use_cases/sql in the official document, I want to replace openai with my API provided by others, what should I do?
### Suggestion:
_No response_
|
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| 1,852,782,550 |
I_kwDOIPDwls5ubzfW
| 9,298 |
Issue: embedding rate limit error
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[] | null | 5 | 2023-08-16T08:42:15 | 2023-11-28T02:28:43 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-uIkxFSWUeCDpCsfzD5XWYLZ7 on tokens per min. Limit: 1000000 / min. Current: 837303 / min. Contact us through our help center at help.openai.com if you continue to have issues..
how can i fix this?
### Suggestion:
_No response_
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I_kwDOIPDwls5ubo0A
| 9,297 |
Error running vector search in Azure Cognitive Search - The 'value' property of the vector query can't be null or an empty array.
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[] | null | 2 | 2023-08-16T08:13:32 | 2023-08-18T00:16:12 | 2023-08-18T00:16:12 |
NONE
| null |
### System Info
When I run a vector search in Azure Cognitive Search using AzureSearch it fails saying, "The 'value' property of the vector query can't be null or an empty array." (full error at the bottom) My code hasn't changed from last week when it used to work. I've got version 11.4.0b6 of azure-search-documents installed. I suspect that Cognitive Search has changed its signature or implementation but the Langchain connection stuff hasn't been updated.
Someone else has reported the same error message but the workaround doesn't work for me (https://github.com/langchain-ai/langchain/issues/7841).
The following is a simplified version of the code which I got from the samples at https://python.langchain.com/docs/integrations/vectorstores/azuresearch. This also fails with the same error.
`import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.azuresearch import AzureSearch
os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"] = '<service_name>'
os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"] = '<index_name>'
os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"] = '<key>'
os.environ["OPENAI_API_KEY"] = '<key2>'
model = 'text-embedding-ada-002'
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "website"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint='https://<service_name>.search.windows.net',
azure_search_key='<key>',
index_name=index_name,
embedding_function=embeddings.embed_query,
)
docs = vector_store.hybrid_search(
query="Test query",
k=3
)
print(docs[0].page_content)
`
The full error is:
Exception has occurred: HttpResponseError
(InvalidRequestParameter) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Parameter name: vector
Code: InvalidRequestParameter
Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Parameter name: vector
Exception Details: (InvalidVectorQuery) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Code: InvalidVectorQuery
Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
File "C:\Work\AI Search\SearchCognitiveSearchScratch.py", line 22, in <module>
docs = vector_store.hybrid_search(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
azure.core.exceptions.HttpResponseError: (InvalidRequestParameter) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Parameter name: vector
Code: InvalidRequestParameter
Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Parameter name: vector
Exception Details: (InvalidVectorQuery) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Code: InvalidVectorQuery
Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run:
pip install azure-search-documents==11.4.0b6
pip install azure-identity
Run (replacing with the keys, service names, index names and service endpoint):
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.azuresearch import AzureSearch
os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"] = '<service_name>'
os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"] = '<index_name>'
os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"] = '<key>'
os.environ["OPENAI_API_KEY"] = '<key2>'
model = 'text-embedding-ada-002'
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "website"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint='https://<service_name>.search.windows.net',
azure_search_key='<key>',
index_name=index_name,
embedding_function=embeddings.embed_query,
)
docs = vector_store.hybrid_search(
query="Test query",
k=3
)
print(docs[0].page_content)
### Expected behavior
Return documents from the index.
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PR_kwDOIPDwls5YCX5H
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bump 266
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[] | null | 1 | 2023-08-16T08:01:50 | 2023-08-16T08:13:20 | 2023-08-16T08:13:19 |
COLLABORATOR
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PR_kwDOIPDwls5YCW1e
| 9,295 |
feat(llms): add streaming support to textgen
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[] | null | 6 | 2023-08-16T07:58:50 | 2023-08-21T14:39:14 | 2023-08-21T14:39:14 |
CONTRIBUTOR
| null |
- Description: Added streaming support to the textgen component in the llms module.
- Dependencies: websocket-client = "^1.6.1"
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Issue: `ApifyWrapper` was removed from the codebase breaking user's code
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[] | null | 3 | 2023-08-16T07:20:48 | 2023-08-31T23:00:59 | 2023-08-31T23:00:59 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
In LangChain 0.0.240, the `ApifyWrapper` class was removed. This caused a breaking change and broke any code using this class.
It was deleted in this PR https://github.com/langchain-ai/langchain/pull/8106 @hwchase17
Could you please outline the reason it was removed?
Other issues related to this one: https://github.com/langchain-ai/langchain/issues/8201 and https://github.com/langchain-ai/langchain/issues/8307
### Suggestion:
I'd suggest adding the integration back so that code written using it will continue to work.
I'm offering to take ownership of the Apify-related code, so if any issue arises, you can just tag me and I'll resolve it.
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PR_kwDOIPDwls5YB0rw
| 9,293 |
added fake vector_store
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[] | null | 8 | 2023-08-16T05:48:59 | 2024-01-04T09:38:32 | null |
CONTRIBUTOR
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#9292
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I_kwDOIPDwls5ua8Ed
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Fake vectorstore
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[] | null | 2 | 2023-08-16T05:48:38 | 2023-11-22T16:06:29 | 2023-11-22T16:06:28 |
CONTRIBUTOR
| null |
### Feature request
It would be nice to have a fake vectorstore to make testing retriaval chains easier.
### Motivation
testing retriaval chains
### Your contribution
yes, I'm happy to help.
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PR_kwDOIPDwls5YBuv-
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Guide import readable json
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[] | null | 2 | 2023-08-16T05:15:39 | 2023-08-16T07:49:01 | 2023-08-16T07:49:01 |
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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Improvements to the Clarifai integration
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[] | null | 5 | 2023-08-16T04:16:51 | 2023-08-21T19:53:37 | 2023-08-21T19:53:37 |
CONTRIBUTOR
| null |
- Improved docs
- Improved performance in multiple ways through batching, threading, etc.
- fixed error message
- Added support for metadata filtering during similarity search.
@baskaryan PTAL
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PR_kwDOIPDwls5YBjXf
| 9,289 |
fix(llms): improve the ernie chat model
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[] | null | 2 | 2023-08-16T04:15:18 | 2023-08-16T07:48:43 | 2023-08-16T07:48:43 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
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: improve the ernie chat model.
- fix missing kwargs to payload
- new test cases
- add some debug level log
- improve description
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
|
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| 1,852,465,014 |
I_kwDOIPDwls5ual92
| 9,288 |
Object of type SystemMessage is not JSON serializable
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[] | null | 3 | 2023-08-16T03:59:44 | 2023-10-17T08:51:14 | null |
CONTRIBUTOR
| null |
### System Info
0.0.247
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
run
```
messages = []
messages.append(SystemMessage(content="abc1"))
json.dumps(messages, ensure_ascii=False)
```
```
File "C:\Users\64478\AppData\Local\Programs\Python\Python310\lib\json\__init__.py", line 238, in dumps
**kw).encode(obj)
File "C:\Users\64478\AppData\Local\Programs\Python\Python310\lib\json\encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "C:\Users\64478\AppData\Local\Programs\Python\Python310\lib\json\encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "C:\Users\64478\AppData\Local\Programs\Python\Python310\lib\json\encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type SystemMessage is not JSON serializable
```
### Expected behavior
I think this might not be a bug
But Would be best if JSON output is (since I am using openai):
```
{"role": "system", "message": "abc1你好"}
```
How can I do this ?
----------
What ai says is not as expected:
```
from langchain.load.dump import dumps
message1 = SystemMessage(content="abc1你好")
message2 = HumanMessage(content="123")
messages = [message1, message1]
print(dumps(messages))
```
is printing:
```json
[{"lc": 1, "type": "constructor", "id": ["langchain", "schema", "messages", "SystemMessage"], "kwargs": {"content": "abc1\u4f60\u597d"}}, {"lc": 1, "type": "constructor", "id": ["langchain", "schema", "messages", "SystemMessage"], "kwargs": {"content": "abc1\u4f60\u597d"}}]
```
And how can I use ensure_ascii with langchain.load.dump ?
|
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PR_kwDOIPDwls5YBdS_
| 9,287 |
Remove summarization from VALID TASKS tuple
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[] | null | 3 | 2023-08-16T03:32:57 | 2023-09-21T21:52:17 | null |
NONE
| null |
In llms.huggingface_endpoint.py, remove summarization from VALID_TASKS because docstring says that summarization is not supported. Also, when trying summarization task, we get an error `KeyError: 'summary_text'` from huggingface endpoint.
resolves #9286
- Twitter handle: @Zorns_Lemma
@baskaryan
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I_kwDOIPDwls5uagXa
| 9,286 |
KeyError: 'summary_text' when trying HuggingFaceEndpoint with task = "summarization" even when the VALID_TASKS include it
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[] | null | 2 | 2023-08-16T03:24:56 | 2023-11-22T16:06:34 | 2023-11-22T16:06:33 |
NONE
| null |
### System Info
LangChain 0.0.265, Mac M2 Pro Hardware. Python 3.10.0
### Who can help?
@hwchase17
@ago
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
llm = HuggingFaceEndpoint(endpoint_url=" task = "summarization",huggingfacehub_api_token
vectorstore_multi_qa = Pinecone(index, embedding_multi_qa.embed_query, "text")
qa_chain = RetrievalQAWithSourcesChain.from_llm(
llm=llm,
#chain_type="stuff",
retriever=vectorstore_multi_qa.as_retriever(),
return_source_documents=True)
query = "What is Blockchain?"
# Send question as a query to qa chain
result = qa_chain({"question": query})
### Expected behavior
Return something
|
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| 1,852,412,897 |
I_kwDOIPDwls5uaZPh
| 9,285 |
Typo in Variable Name in script_chain.run()
|
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[] | null | 1 | 2023-08-16T02:40:14 | 2023-11-22T16:06:39 | 2023-11-22T16:06:38 |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.265
Python 3.10.7
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
prompt=st.text_input("enter search key")
llm=OpenAI(temperature=0.9)
script_memory=ConversationBufferMemory(input_key='title', memory_key='history')
script_template=PromptTemplate(
input_variables=['title', 'wikiepedia_research'],
template='Write me Youtube video script based on this TITLE: {title} while leveraging this wikipedia research: {wikiepedia_research}'
)
script_chain=LLMChain(llm=llm, prompt=script_template, verbose=True, output_key='script',memory=script_memory)
wiki = WikipediaAPIWrapper()
wiki_research = wiki.run(prompt)
# key should be wikipedia_research
script = script_chain.run(title=title, wikiepedia_research=wiki_research)
### Expected behavior
script = script_chain.run(title=title, wikipedia_research=wiki_research)
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| 1,852,397,983 |
I_kwDOIPDwls5uaVmf
| 9,284 |
If values["document_variable_name"] happens to be equal to one of the variable names in llm_chain_variables, the if condition check would fail, skipping the error throw.
|
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[] | null | 1 | 2023-08-16T02:16:41 | 2023-08-16T02:19:58 | 2023-08-16T02:19:40 |
NONE
| null |
https://github.com/langchain-ai/langchain/blob/e986afa13a1de73f403eebe05bd4b25781c12788/libs/langchain/langchain/chains/combine_documents/stuff.py#L96C76-L96C76
If values["document_variable_name"] happens to be equal to one of the variable names in llm_chain_variables, the if condition check would fail, skipping the error throw.
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PR_kwDOIPDwls5YBE_T
| 9,283 |
Adds streaming for runnable maps
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CONTRIBUTOR
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@nfcampos @baskaryan
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I_kwDOIPDwls5uaE5Z
| 9,282 |
DOC: missed items in the `API Reference:` auto-generated section
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[] | null | 2 | 2023-08-16T00:40:19 | 2023-11-15T16:41:49 | 2023-11-15T16:41:42 |
COLLABORATOR
| null |
### Issue with current documentation:
See this page: https://python.langchain.com/docs/use_cases/multi_modal/image_agent
The `from langchain import OpenAI` class was not extracted into the `API Reference:` section.
My guess is that was because the `langchain import` doesn't have `.` in the namespace. Other classes with `dot` were extracted.
Another example: https://python.langchain.com/docs/use_cases/more/code_writing/llm_math
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5YA_EI
| 9,281 |
📖 docs: fixed `integrations/document loaders` toc
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[] | null | 9 | 2023-08-16T00:03:29 | 2023-09-26T16:47:38 | 2023-09-26T16:47:38 |
COLLABORATOR
| null |
Fixed navbar:
- renamed several files, so ToC is sorted correctly
- made ToC items consistent: formatted several Titles
- added several links
- reformatted several docs to a consistent format
- renamed several files (removed `_example` suffix)
- added renamed files to the `docs/docs_skeleton/vercel.json`
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PR_kwDOIPDwls5YA7R3
| 9,280 |
Cassandra Vector Store, add metadata filtering + improvements
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[] | null | 5 | 2023-08-15T23:38:44 | 2023-09-18T16:18:51 | 2023-09-13T21:18:39 |
CONTRIBUTOR
| null |
This PR addresses a few minor issues with the Cassandra vector store implementation and extends the store to support Metadata search.
Thanks to the latest cassIO library (>=0.1.0), metadata filtering is available in the store.
Further,
- the "relevance" score is prevented from being flipped in the [0,1] interval, thus ensuring that 1 corresponds to the closest vector (this is related to how the underlying cassIO class returns the cosine difference);
- bumped the cassIO package version both in the notebooks and the pyproject.toml;
- adjusted the textfile location for the vector-store example after the reshuffling of the Langchain repo dir structure;
- added demonstration of metadata filtering in the Cassandra vector store notebook;
- better docstring for the Cassandra vector store class;
- fixed test flakiness and removed offending out-of-place escape chars from a test module docstring;
To my knowledge all relevant tests pass and mypy+black+ruff don't complain. (mypy gives unrelated errors in other modules, which clearly don't depend on the content of this PR).
Thank you!
Stefano
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PR_kwDOIPDwls5YA7LD
| 9,279 |
update guide import
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PR_kwDOIPDwls5YA6m4
| 9,278 |
update openai functions docs
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| 1,852,278,745 |
PR_kwDOIPDwls5YA5SO
| 9,277 |
📖 docs: fixed `integration/llms` navbar
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| null |
Fixed navbar:
- renamed several files, so ToC is sorted correctly
- made ToC items consistent: formatted several Titles
- added several links
- reformatted several docs to a consistent format
- renamed several files (removed `_example` suffix)
- added renamed files to the `docs/docs_skeleton/vercel.json`
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PR_kwDOIPDwls5YA1Xd
| 9,276 |
Bagatur/vectara regression
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PR_kwDOIPDwls5YAw1B
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Bagatur/deeplake docs fixes
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PR_kwDOIPDwls5YAjQz
| 9,274 |
pin pydantic
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| null |
don't want default to be v2 yet
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I_kwDOIPDwls5uZShX
| 9,273 |
How do fix GPT2 Tokenizer error in Langchain map_reduce (LLama2)?
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[] | null | 6 | 2023-08-15T21:01:48 | 2024-01-01T18:11:07 | 2023-12-25T16:09:14 |
NONE
| null |
### System Info
I'm using AWS Sagemaker Jumpstart model for Llama2 13b: meta-textgeneration-llama-2-13b-f
On running a Langchain summarize chain with chain_type="map_reduce" I get the below error. Other chain types (refine, stuff) work without issues. I do not have access to https://huggingface.co/ from my environment. Is there a way to set the gpt2 tokenizer in a local dir?
```
parameters = {
"properties": {
"min_length": 100,
"max_length": 1024,
"do_sample": True,
"top_p": 0.9,
"repetition_penalty": 1.03,
"temperature": 0.8
}
}
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({"inputs": prompt, **model_kwargs})
print(input_str)
return input_str.encode("utf-8")
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generation"]["content"]
content_handler = ContentHandler()
endpoint_name='xxxxxxxxxxxxxxxxxx'
llm=SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name="us-east-1",
model_kwargs=parameters,
content_handler=content_handler,
endpoint_kwargs={"CustomAttributes": 'accept_eula=true'}
)
chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs)
```
Error:
```
File /usr/local/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1788, in PreTrainedTokenizerBase.from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)
1782 logger.info(
1783 f"Can't load following files from cache: {unresolved_files} and cannot check if these "
1784 "files are necessary for the tokenizer to operate."
1785 )
1787 if all(full_file_name is None for full_file_name in resolved_vocab_files.values()):
-> 1788 raise EnvironmentError(
1789 f"Can't load tokenizer for '{pretrained_model_name_or_path}'. If you were trying to load it from "
1790 "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
1791 f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
1792 f"containing all relevant files for a {cls.__name__} tokenizer."
1793 )
1795 for file_id, file_path in vocab_files.items():
1796 if file_id not in resolved_vocab_files:
OSError: Can't load tokenizer for 'gpt2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'gpt2' is the correct path to a directory containing all relevant files for a GPT2TokenizerFast tokenizer.
```
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Provided above
### Expected behavior
Provided above
|
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| 9,272 |
Exclude in Generic from_filesystem
|
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CONTRIBUTOR
| null |
Description: add in exclude parameter to GenericLoader classmethod from_filesystem
Issue: Didn't get added in https://github.com/langchain-ai/langchain/pull/9064
Dependencies: None
|
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| 1,852,108,322 |
I_kwDOIPDwls5uZO4i
| 9,271 |
Stream the response of the custom tools in the agent
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[] | null | 3 | 2023-08-15T20:48:35 | 2023-11-22T16:06:44 | 2023-11-22T16:06:43 |
NONE
| null |
### Issue you'd like to raise.
llm = ChatOpenAI(temperature=0, model_name = args.ModelName, verbose = True, streaming = True, callbacks = [MyCallbackHandler(new_payload)])
class StaticSearchTool(BaseTool):
name = "Search_QA_System"
description = "Use this tool to answer cuurent events
# return_direct = True
def _run(
self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
"""Use the tool."""
return qa.run(query)
async def _arun(
self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
) -> str:
"""Use the tool asynchronously."""
return await qa.arun(query)
def define_search_tool():
search = [StaticSearchTool()]
return search
agent_executor = initialize_agent(
define_search_tool(),
llm,
system_message = system_message,
extra_prompt_messages = [MessagesPlaceholder(variable_name="memory")],
agent_instructions = "Analyse the query closely and decide the query intend. If the query requires internet to search than use google-serper to answer the query. Try to invoke `google-serper` most of the times",
agent=AgentType.OPENAI_FUNCTIONS,
agent_kwargs= agent_kwargs,
verbose = True,
memory = memory,
handle_parsing_errors=True,
)
In this code I need to stream the output of the StaticSearchTool() not the output of the agent.
How can we do this
### Suggestion:
_No response_
|
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I_kwDOIPDwls5uZJpz
| 9,270 |
Issue: openai.error.PermissionError: You are not allowed to generate embeddings from this model
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[] | null | 5 | 2023-08-15T20:31:54 | 2023-10-29T13:09:23 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I have an issue when trying to set 'gpt-3.5-turbo' as a model to create embeddings.
When using the 'gpt-3.5-turbo' to create LLM, everything works fine:
`llm = OpenAI(model_name='gpt-3.5-turbo', temperature=0, openai_api_key=OPENAI_API_KEY, max_tokens=512)`
Also creating embeddings without specifying a model works fine:
`embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)`
But when I try to use 'gpt-3.5-turbo' for embeddings, I get the following error:
`embeddings = OpenAIEmbeddings(model='gpt-3.5-turbo', openai_api_key=OPENAI_API_KEY)`
Error:
_openai.error.PermissionError: You are not allowed to generate embeddings from this model_
This seems weird to me since I have just used the same model to create LLM. So I have access to it and should be able to use it. Unless creation of embeddings and LLM differs somehow.
Can somebody please advise?
### Suggestion:
_No response_
|
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| 1,852,080,565 |
PR_kwDOIPDwls5YANcE
| 9,269 |
Fix region sagemaker
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[] | null | 16 | 2023-08-15T20:26:41 | 2023-11-13T21:13:19 | 2023-11-13T21:13:18 |
NONE
| null |
I encountered an issue while working with the Sagemaker endpoint module and attempting to establish a connection to the client.
The error message `"Failed to load credentials for AWS client authentication`" was being raised, despite passing the **`profile_name`** parameter.
Upon reviewing the code, I realized that the error was caused by the omission of the **`region_name`** parameter. To address this, I introduced a validation step to ensure that the **`region_name`** parameter is provided, and I implemented an error-raising mechanism in case the parameter is found to be `None`.
This adjustment effectively resolved the issue.
Thanks!
|
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| 1,852,064,945 |
PR_kwDOIPDwls5YAJ-o
| 9,268 |
Random compatibility issues
|
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[] | null | 2 | 2023-08-15T20:16:24 | 2023-09-21T21:50:09 | null |
COLLABORATOR
| null |
A fix for random issues like this:
```
from pydantic import BaseModel, Field # <-- may be v2 namespace
class CalculatorInput(BaseModel):
question: str = Field()
Tool.from_function( # <-- tool uses v1 namespace
func=lambda question: 'hello',
name="Calculator",
description="useful for when you need to answer questions about math",
args_schema=CalculatorInput
)
```
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PR_kwDOIPDwls5YADQ5
| 9,267 |
fix context nb
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[] | null | 1 | 2023-08-15T19:52:47 | 2023-08-15T20:25:52 | 2023-08-15T19:53:38 |
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PR_kwDOIPDwls5X__5K
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Open source LLM guide
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[] | null | 3 | 2023-08-15T19:39:41 | 2023-08-16T23:18:32 | 2023-08-16T23:18:31 |
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Guide for using open source LLMs locally.
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Missing some input keys: {'context'} when using ConversationalRetrievalChain
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[] | null | 5 | 2023-08-15T19:31:45 | 2023-11-21T16:27:42 | null |
NONE
| null |
I am trying to create a `ConversationalRetrievalChain` with memory, `return_source_document=True` and a custom retriever which returns content and url of the document. I am able to generate the right response when I call the chain for the first time. But when I call it again with memory I am getting error Missing some input keys: {'context'}
Here is the trace of the error.
```
Missing some input keys: {'context'}
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[470], line 1
----> 1 contracts_chain("How can I apply for a TPA?")
File /mnt/xarfuse/uid-560402/653248bf-seed-nspid4026531836_cgpid28389965-ns-4026531840/langchain/chains/base.py:258, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
256 except (KeyboardInterrupt, Exception) as e:
257 run_manager.on_chain_error(e)
--> 258 raise e
259 run_manager.on_chain_end(outputs)
260 final_outputs: Dict[str, Any] = self.prep_outputs(
261 inputs, outputs, return_only_outputs
262 )
File /mnt/xarfuse/uid-560402/653248bf-seed-nspid4026531836_cgpid28389965-ns-4026531840/langchain/chains/base.py:252, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
246 run_manager = callback_manager.on_chain_start(
247 dumpd(self),
248 inputs,
249 )
250 try:
251 outputs = (
--> 252 self._call(inputs, run_manager=run_manager)
253 if new_arg_supported
254 else self._call(inputs)
255 )
256 except (KeyboardInterrupt, Exception) as e:
257 run_manager.on_chain_error(e)
File /mnt/xarfuse/uid-560402/653248bf-seed-nspid4026531836_cgpid28389965-ns-4026531840/langchain/chains/conversational_retrieval/base.py:126, in BaseConversationalRetrievalChain._call(self, inputs, run_manager)
124 if chat_history_str:
125 callbacks = _run_manager.get_child()
--> 126 new_question = self.question_generator.run(
127 question=question, chat_history=chat_history_str, callbacks=callbacks
128 )
129 else:
130 new_question = question
File /mnt/xarfuse/uid-560402/653248bf-seed-nspid4026531836_cgpid28389965-ns-4026531840/langchain/chains/base.py:456, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
451 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
452 _output_key
453 ]
455 if kwargs and not args:
--> 456 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
457 _output_key
458 ]
460 if not kwargs and not args:
461 raise ValueError(
462 "`run` supported with either positional arguments or keyword arguments,"
463 " but none were provided."
464 )
File /mnt/xarfuse/uid-560402/653248bf-seed-nspid4026531836_cgpid28389965-ns-4026531840/langchain/chains/base.py:235, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
200 def __call__(
201 self,
202 inputs: Union[Dict[str, Any], Any],
(...)
208 include_run_info: bool = False,
209 ) -> Dict[str, Any]:
210 """Execute the chain.
211
212 Args:
(...)
233 `Chain.output_keys`.
234 """
--> 235 inputs = self.prep_inputs(inputs)
236 callback_manager = CallbackManager.configure(
237 callbacks,
238 self.callbacks,
(...)
243 self.metadata,
244 )
245 new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
File /mnt/xarfuse/uid-560402/653248bf-seed-nspid4026531836_cgpid28389965-ns-4026531840/langchain/chains/base.py:389, in Chain.prep_inputs(self, inputs)
387 external_context = self.memory.load_memory_variables(inputs)
388 inputs = dict(inputs, **external_context)
--> 389 self._validate_inputs(inputs)
390 return inputs
File /mnt/xarfuse/uid-560402/653248bf-seed-nspid4026531836_cgpid28389965-ns-4026531840/langchain/chains/base.py:147, in Chain._validate_inputs(self, inputs)
145 missing_keys = set(self.input_keys).difference(inputs)
146 if missing_keys:
--> 147 raise ValueError(f"Missing some input keys: {missing_keys}")
ValueError: Missing some input keys: {'context'}
```
Here is my code
```
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key="answer", input_key="question")
llama2_chat_prompt = """
<s>[INST] <<SYS>>
You are a helpful assistant for tech company.
Answer question using the following conversation history and context
Conversation History: {chat_history}
Context: {context}
<</SYS>>
Question: {question}
Answer in Markdown:
<<SYS>>
<</SYS>>
[/INST]
"""
prompt = PromptTemplate(
input_variables=["question", "context", "chat_history"],
output_parser=None,
partial_variables={},
template=llama2_chat_prompt,
template_format="f-string",
validate_template=False,
verbose=verbose,
)
llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
doc_input_variables = ["page_content", "src"]
document_prompt = PromptTemplate(
input_variables=doc_input_variables,
template="Content: {page_content}\nSource: {src}",
)
combine_documents_chain = StuffDocumentsChain(
llm_chain=llm_chain,
document_variable_name="context",
document_prompt=document_prompt,
verbose=verbose,
)
contracts_chain = ConversationalRetrievalChain(
retriever=my_custom_retriver,
memory=memory,
question_generator=llm_chain,
combine_docs_chain=combine_documents_chain,
return_source_documents=False,
verbose=verbose,
)
```
resp = contracts_chain("Can you help me?") ## runs fine
resp1 = contracts_chain("My question is about weather") ## gives error.
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