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I_kwDOIPDwls50mIm8
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AttributeError: partially initialized module 'arxiv' has no attribute 'Search' (most likely due to a circular import)
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| 2023-12-06T02:37:31
| 2023-10-25T15:54:34
|
NONE
| null |
### System Info
version:
- langchain-0.0.320
- py311
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Running the [example code](https://python.langchain.com/docs/integrations/retrievers/arxiv)
```python
from langchain.retrievers import ArxivRetriever
retriever = ArxivRetriever(load_max_docs=2)
docs = retriever.get_relevant_documents(query="1605.08386")
```
The above code gives the error:
`AttributeError: partially initialized module 'arxiv' has no attribute 'Search' (most likely due to a circular import)`
### Expected behavior
Should print out
```
{'Published': '2016-05-26',
'Title': 'Heat-bath random walks with Markov bases',
'Authors': 'Caprice Stanley, Tobias Windisch',
'Summary': 'Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.'}
```
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I_kwDOIPDwls50mG_o
| 12,142
|
latest version langchain locks peer dependency: "googleapis" version to 126.0.1
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| 2023-10-23T01:01:39
| null |
NONE
| null |
### System Info
latest version langchain locks peer dependency: "googleapis" version to 126.0.1, wondering why it has to be googleapis v126.0.1. it introduces tech debts.
Please fix the peer dep version for `googleapis` if there is no specific reasons.
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
when try to install both latest version langchain and googleapis latest version in nodejs via npm.
### Expected behavior
langchain shouldn't prevent latest googleapis to be installed.
|
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| 1,956,142,284
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PR_kwDOIPDwls5defCQ
| 12,141
|
Fix Pickle Error
|
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| 2023-10-23T00:48:48
| 2023-10-23T15:22:48
| 2023-10-23T15:22:47
|
COLLABORATOR
| null |
If non-pickleable objects (like locks) get passed to the tracing callback, they'll fail in the deepcopy. Fallback to a shallow copy in these instances .
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PR_kwDOIPDwls5deYHI
| 12,140
|
updated API Reference `langchain.agents` functions table
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| 2023-11-15T02:02:18
| 2023-11-15T01:22:40
|
COLLABORATOR
| null |
The `agents.agent_toolkits.conversational_retrieval.openai_functions.create_conversational_retrieval_agent` is a too long name.
It makes the ['agents/functions' table](https://api.python.langchain.com/en/latest/api_reference.html#id2) inside API Reference unreadable.
I've hidden this function from the API Reference.
Note: This is a temporary solution! There should be more elegant solution.
|
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PR_kwDOIPDwls5deXPr
| 12,139
|
Include Parent Run ID
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| 2023-10-22T23:55:57
| 2023-10-23T00:19:12
| 2023-10-23T00:19:11
|
COLLABORATOR
| null |
If you set local callbacks
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I_kwDOIPDwls50lkQc
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|
DOC: Example from Custom Tool documentation fails
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| 2023-10-22T18:25:42
| 2023-10-22T18:48:16
| 2023-10-22T18:48:15
|
NONE
| null |
### Issue with current documentation:
On this page
This example:
```
class SearchInput(BaseModel):
query: str = Field(description="should be a search query")
@tool("search", return_direct=True, args_schema=SearchInput)
def search_api(query: str) -> str:
"""Searches the API for the query."""
return "Results"
search_api
```
Fails with:
ValidationError: 1 validation error for StructuredTool
args_schema
subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel)
### Idea or request for content:
nothing missing, but example fails
Name: langchain
Version: 0.0.311
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PR_kwDOIPDwls5deC3d
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Fix bug where types were lost when calling with_cconfig or bind
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5deCRi
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Improved readability of Docs
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CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** a description of the change,
improved grammar and readability of DOCS
@hwchase17
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PR_kwDOIPDwls5deAmR
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Add how-to guide on runnable generators
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5dd-PR
| 12,134
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Update default recursion_limit, update docs
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
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PR_kwDOIPDwls5dd-C7
| 12,133
|
Remove GetLocal, PutLocal
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COLLABORATOR
| null |
Do you agree?
|
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PR_kwDOIPDwls5dd893
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Fix type hint for older py versions
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5dd8w_
| 12,131
|
Add optional config arg to RunnablePassthrough func arg
|
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5ddY6v
| 12,128
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Fix typos
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CONTRIBUTOR
| null |
Fix typos
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I_kwDOIPDwls50ksS-
| 12,127
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DOC: Issue Connecting to Docusaurus in Local Container Environment
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[] | null | 0
| 2023-10-22T06:06:45
| 2023-10-22T06:08:53
| null |
NONE
| null |
### Issue with current documentation:
When starting Docusaurus in a local container environment using the `make docs_build` command, it runs at `localhost:3000`, but a connection from the host machine is not possible.
The container has both `poetry` and `yarn` installed.
Even though port 3000 is forwarded to the host, the connection is not established.
https://github.com/langchain-ai/langchain/blob/master/docs/package.json#L7
I am proficient with Python but struggle with React, which might be influencing my troubleshooting.
Thank you!!
### Idea or request for content:
By adding the `--host 0.0.0.0` option to the `start` command in `package.json`, the issue might be resolved.
```diff
-"start": "rm -rf ./docs/api && docusaurus start",
+"start": "rm -rf ./docs/api && docusaurus start --host 0.0.0.0",
```
- With this change, it would be possible to access the port that's being forwarded from within the container to the host environment.
- However, using the `--host 0.0.0.0` option can have security implications, so caution is advised especially in production environments.
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PR_kwDOIPDwls5ddVqL
| 12,126
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Fixed hardcoded "vector" and replaced with vector_query_field variable
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| 2023-10-23T19:08:55
| 2023-10-23T19:08:55
|
CONTRIBUTOR
| null |
- **Description:** In the max_marginal_relevance_search function of the ElasticsearchStore vector store, the name of the field corresponding to the vector embedding of the document is hard coded in the delete statement that drops the field from the document metadata. This results in an exception if the vector embedding field is customized. This PR changes the hard-coded "vector" into the vector_query_field variable.
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
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I_kwDOIPDwls50kn0v
| 12,125
|
Is there any loader for Microsoft OneNote like EverNoteLoader or ObsidianLoader
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| 2023-10-22T04:36:08
| 2023-11-04T15:15:51
| null |
NONE
| null |
### Feature request
Microsoft OneNote loader like EverNoteLoader or ObsidianLoader
### Motivation
N/A
### Your contribution
N/A
|
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I_kwDOIPDwls50knnS
| 12,124
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Issue: `VectorDBQA` is a better chain than `RetrievalQA` when it comes to streaming model response
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[] | null | 1
| 2023-10-22T04:31:46
| 2023-10-22T04:38:05
| null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I found that using a `RetrievalQA` for streaming outputs gibberish response. For example, using a `RetrievalQA` with code below on the `state_of_the_union.txt` example:
```
doc_chain = load_qa_chain(
llm=ChatOpenAI(
streaming=True,
openai_api_key=api_key,
callbacks=[StreamingStdOutCallbackHandler()]
),
chain_type="map_reduce",
verbose=False,
)
retrieval_qa = RetrievalQA(
combine_documents_chain=doc_chain,
retriever=retriever,
)
```
With this call: `retrieval_qa.run("What did the president say about Ketanji Brown Jackson")` outputs this streamed response:
```
"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.""And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.""And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence."The given portion of the document does not mention anything about what the president said about Ketanji Brown Jackson.The given portion of the document does not provide any information about what the president said about Ketanji Brown Jackson.
```
While using `VectorDBChain`:
```
qa = VectorDBQA.from_chain_type(
llm=ChatOpenAI(
streaming=True,
openai_api_key=api_key,
callbacks=[StreamingStdOutCallbackHandler()]
),
chain_type="map_reduce",
vectorstore=db
)
```
Outputs this:
```
The President said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, who is one of our nation's top legal minds and will continue Justice Breyer's legacy of excellence.
```
Is there any reason as to why the `VectorDBQA` chains have been deprecated in favour of the `RetrievalQA` chains? At first I thought the gibbering streaming was related to using PGVector, but I even tried it with Chroma and am having the same issue.
### Suggestion:
_No response_
|
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| 1,955,751,254
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PR_kwDOIPDwls5ddR8_
| 12,123
|
Fix bug in doctran document transformers
|
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| 2023-10-22T04:04:48
| 2023-12-28T16:06:13
| 2023-12-28T16:06:12
|
CONTRIBUTOR
| null |
The latest doctran library no longer supports asynchrony.
The doctran library has removed support for asynchrony during this modification.
https://github.com/psychic-api/doctran/commit/22ec68d1fefaf3a141083489452ed0f82cb03a88
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PR_kwDOIPDwls5ddIRX
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|
a bug fix for sagemakerendpoint streaming
|
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[] | null | 4
| 2023-10-21T23:52:11
| 2023-10-27T02:14:15
| 2023-10-26T05:36:10
|
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a bug fix for sagemaker endpoint streaming,
- **Issue:** the issue # it fixed _call function so that it can now stream from TGI container as well and added _acall stream function for both LMI or TGI containers,
- **Dependencies:** no dependencies,
- **Tag maintainer:** @3coins ,
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5ddFQB
| 12,121
|
add rag fusion notebook
|
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| 2023-10-21T22:37:11
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COLLABORATOR
| null | null |
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I_kwDOIPDwls50kPTf
| 12,120
|
Feature: Steam game recommendation tool
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[] | null | 8
| 2023-10-21T20:44:10
| 2023-12-15T04:37:11
| 2023-12-15T04:37:11
|
CONTRIBUTOR
| null |
### Feature request
Feature request to Integrate a new Langchain tool that make recommandtions on steam games based on user's Steam ID and provide game information based on given Steam game name.
### Motivation
We recognize the current challenges users face when discovering games on Steam—cluttered search results and lack of tailored information. To revolutionize this experience, we propose integrating Langchain, offering personalized game recommendations based on a user's Steam ID and comprehensive game details based on specific titles.
Langchain's algorithm will provide personalized game suggestions aligned with users' preferences, eliminating irrelevant options. It'll also furnish detailed game insights—prices, discounts, popularity, and latest news—for informed decision-making.
This integration aims to streamline game discovery, enhance user satisfaction, and foster a vibrant gaming community on Steam. We're eager to discuss this enhancement and its potential to transform the Steam experience.
### Your contribution
We are a group of 4 looking to contribute to Langchain as a course project and will be creating a PR for this issue in mid November.
|
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PR_kwDOIPDwls5dc_gb
| 12,119
|
fix up notebook
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I_kwDOIPDwls50kNBR
| 12,118
|
ConversationalRetrievalChain cannot return source documents when using ConversationBufferWindowMemory
|
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[] | null | 2
| 2023-10-21T20:06:18
| 2023-10-22T00:11:00
| 2023-10-22T00:11:00
|
NONE
| null |
### System Info
Langchain 0.0.285, python 3.11.5, Windows 11
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
``` python
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import TextLoader
from langchain.memory import ConversationBufferMemory
from mysecrets import secrets
loader = TextLoader("./sotu.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(documents)
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
embed_instruction = "Represent the document for summary"
embedding_function = HuggingFaceInstructEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
embed_instruction = embed_instruction
)
vectorstore = Chroma.from_documents(documents, embedding_function)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm = OpenAI(openai_api_key=secrets["OPENAI-APIKEY"],temperature=0)
qa = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory, return_source_documents=True)
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query})
```
### Expected behavior
When `return_source_documents` is set to False, code runs as intended. When ConversationalRetrievalChain uses the default value for `memory`, code runs as intended. If ConversationalRetrievalChain is used with memory and source documents are to be returned, the code fails since chat_memory.py (https://github.com/langchain-ai/langchain/blame/master/libs/langchain/langchain/memory/chat_memory.py) is expecting only one key.
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I_kwDOIPDwls50kKvq
| 12,117
|
Architecture: Decouple Configuration from Inheritance for better extensibility
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[] | null | 1
| 2023-10-21T19:33:24
| 2023-10-21T19:35:55
| null |
NONE
| null |
### Feature request
There seems to be a conflation between configuration code and inheritance trees that make it nearly impossible to extend langchain functionality without recreating entire class inheritance. I ran into this almost immediately when trying to customize agents and executors to my application needs, but also take advantage of the pre-assembled styles (e.g. Zero-Shot react). I had to recreate my style of zero shot executor and agent by copying the parameters from the subclasses into my forked instance of the bases.
My proposal is to decouple these two concepts and have a single inheritance tree for functionality (e.g. agents, chains) and a separate notion of a “config” class that can hold the parameterization for an object. The factory methods on the core classes can then accept a config parameter to create an instance with the desired parameterization. This enables extension in two dimensions - any core object (agent, chain, etc) x any configuration. Furthermore new configurations can easily be created by end users or even extensions of existing ones (maybe you want to swap out one parameter from a predefined config without copy pasting the whole code).
### Motivation
Architectural improvement and future extensibility of the framework
### Your contribution
PR forthcoming with a POC when I have some time to abstract what I did for my projects into the langchain source but wanted to seed this for discussion!
|
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| 12,116
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SendGrid API Integration For Enhanced Email Communication
|
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| 2023-10-21T19:00:04
| 2023-10-21T19:02:18
| null |
CONTRIBUTOR
| null |
### Feature request
Feature request to integrate the SendGrid API into Langchain for enhanced email communication and notifications.
**Relevant links:**
1. [SendGrid API Documentation](https://sendgrid.com/docs/API_Reference/index.html): The official documentation for the SendGrid API, offering comprehensive information on how to use the API for sending emails and managing email communication.
2. [SendGrid GitHub Repository](https://github.com/sendgrid/sendgrid-python): The GitHub repository for SendGrid's official Python library, which can be utilized for API integration.
### Motivation
Langchain is committed to offering a seamless platform for language and communication-related tools. By integrating the SendGrid API, we aim to bring advanced email communication capabilities to the platform, enhancing its utility and user experience.
SendGrid is a renowned email platform that provides a reliable and efficient way to send, receive, and manage emails. By incorporating the SendGrid API into Langchain, we enable users to send emails, manage notifications, and enhance their communication within the platform.
Our goal is to provide Langchain users with a feature-rich email system that allows them to send notifications, updates, and alerts directly from Langchain. This integration will streamline communication, offering users a seamless experience and the ability to keep stakeholders informed and engaged through email.
In summary, the SendGrid API integration project is designed to extend Langchain's capabilities by incorporating a powerful email communication tool. This will help users effectively manage email notifications and stay connected with stakeholders within the platform.
### Your contribution
We are 4 University of Toronto students and are very interested in contributing to Langchain for a course project. We will be
creating a PR that implements this feature sometime in mid November.
|
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Stack Exchange API Integration
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[] | null | 1
| 2023-10-21T18:49:17
| 2023-10-21T18:51:49
| null |
CONTRIBUTOR
| null |
### Feature request
Request to Integrate Stack Exchange API into Langchain for enhanced information access and interaction.
**Relevant links:**
1. **Stack Exchange API Documentation**: The official documentation for the Stack Exchange API, providing detailed information on how to interact with the API and retrieve data.
[Stack Exchange API Documentation](https://api.stackexchange.com/docs)
2. **Stack Exchange API Client GitHub Repository**: Community-supported libraries or tools that interact with the API.
[Stack Exchange API Client GitHub Repository](https://github.com/benatespina/StackExchangeApiClient)
### Motivation
Langchain aims to provide a comprehensive and seamless platform for accessing and interacting with a wide array of linguistic and language-related resources. In this context, integrating the Stack Exchange API would greatly enhance the utility of the platform.
Stack Exchange hosts a plethora of specialized communities, each dedicated to specific domains of knowledge and expertise. These communities generate valuable content in the form of questions, answers, and discussions. By integrating the Stack Exchange API into Langchain, we can enable users to access this wealth of information directly from within the platform.
What we aim to provide is a convenient gateway for Langchain users to explore Stack Exchange communities, search for relevant questions and answers, and actively participate in discussions. This integration would not only simplify the process of finding authoritative information on various topics but also facilitate direct engagement with experts and enthusiasts in their respective fields.
In summary, our Stack Exchange API integration project is designed to enrich the Langchain experience by offering a direct link to the vast knowledge repositories of Stack Exchange communities. It will empower users to seamlessly navigate between these platforms, harnessing the collective wisdom of these communities for their linguistic and language-related endeavors.
### Your contribution
We are 4 University of Toronto students and are very interested in contributing to Langchain for a course project. We will be
creating a PR that implements this feature sometime in mid November.
|
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| 12,114
|
add missing init files
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5dcxn9
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|
move csv agent to langchain experimental
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| 2023-10-21T17:26:03
| 2023-10-21T17:26:02
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I_kwDOIPDwls50j7gq
| 12,112
|
Issue with Exception Handling: UnstructuredURLLoader Does Not Raise Exceptions Inheriting from Base Class Exception
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open
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[] | null | 1
| 2023-10-21T16:52:54
| 2023-10-21T16:59:51
| null |
NONE
| null |
### System Info
#### Description:
I encountered an issue with the UnstructuredURLLoader class from the "langchain" library, specifically in the libs/langchain/langchain/document_loaders/url.py module. When trying to handle exceptions for a failed request, I observed that the exception raised by the library doesn't inherit from the base class Exception. This makes it challenging to handle exceptions properly in my code.
#### **Actual Behavior**:
The exception raised by the **`UnstructuredURLLoader`** doesn't inherit from the base class **`Exception`**, causing difficulty in handling exceptions effectively.
#### **Error Message**:
```Error fetching or processing https://en.wikipesdfdia.org/, exception: HTTPSConnectionPool(host='en.wikipesdfdia.org', port=443): Max retries exceeded with url: / (Caused by NameResolutionError("<urllib3.connection.HTTPSConnection object at 0x000002113AD12F10>: Failed to resolve 'en.wikipesdfdia.org' ([Errno 11001] getaddrinfo failed)"))```
### Who can help?
@eyurtsev @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```py
from langchain.document_loaders import UnstructuredURLLoader
from urllib3 import HTTPSConnectionPool
url = 'https://en.wikipesdfdia.org/'
try:
unstructured_loader = UnstructuredURLLoader([url])
all_content = unstructured_loader.load()
print("Request was successful")
except HTTPSConnectionPool as e:
# Handle HTTPConnectionPool exceptions
print(f"An HTTPConnectionPool exception occurred: {e}")
except Exception as e:
# Handle other exceptions
print(f"An unexpected error occurred: {e}")
```
### Expected behavior
I expected the exception raised by the **`UnstructuredURLLoader`** to be derived from the base class **`Exception`**, which would allow for proper exception handling.
|
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rewrite
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I_kwDOIPDwls50j2E8
| 12,110
|
API Key Leakage in the BaseLanguageModel
|
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| false
| null |
[] | null | 2
| 2023-10-21T16:08:21
| 2023-10-28T22:39:53
| null |
NONE
| null |
### System Info
Langchain:0.0.319
Python:3.11.2
System: macOS 13.5.2 arm64
### 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
I implemented a generic module to handle many types of chat models. I executed the following script after I set `OPENAI_API_KEY=xxx` in `.env` file.
```python
from dataclasses import dataclass, field
from langchain.schema.language_model import BaseLanguageModel
from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
@dataclass
class ModelConfig:
"""Model Configuration"""
model: BaseLanguageModel
arguments: field(
default_factory=dict
) # This could be any arguments for BaseLanguageModel child class
def init_model(self) -> BaseLanguageModel:
return self.model(**self.arguments)
if __name__ == "__main__":
model = ModelConfig(model=ChatOpenAI, arguments=dict())
print(model)
```
And then the output contains **a leaked API key**!
```sh
<class 'openai.api_resources.chat_completion.ChatCompletion'> openai_api_key=xxxx>
```
### Expected behavior
My idea off the top of my head is to use, say, `__str__` directive inside the original class. But this seems just a workaround for ChatOpenAI class. So something more generic feature in `BaseLanguageModel` would be more decent. Like should have a common property in the base class, which handle API token across any LLM models and this property might be a special class that can hide the value when other class and functions call this.
https://github.com/langchain-ai/langchain/blob/08f5e6b8012f5eda2609103f33676199a3781a15/libs/langchain/langchain/chat_models/openai.py#L181-L238
## Similar issue.
#8499
|
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PR_kwDOIPDwls5dcqAQ
| 12,109
|
Update Tavily API key link
|
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| 2023-10-21T15:41:32
| 2023-10-21T15:53:06
| 2023-10-21T15:44:58
|
CONTRIBUTOR
| null |
fix broken link to generate tavily api key
|
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bump 320
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Bagatur/e2b integration
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PR_kwDOIPDwls5dcmfc
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10634: Added the capability to inject boto3 client in SagemakerEndpointEmbeddings
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**Description: Allow to inject boto3 client for Cross account access type of scenarios in using SagemakerEndpointEmbeddings and also updated the documentation for same in the sample notebook**
Issue:SSagemakerEndpointEmbeddings cross account capability #10634 #10184
Dependencies: None
Tag maintainer:
Twitter handle:lethargicoder
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PR_kwDOIPDwls5dckHN
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Create CODE_OF_CONDUCT.md
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code of conduct.md file is missing it is generally present in good repos which have large community
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PR_kwDOIPDwls5dciTL
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Create CODE_OF_CONDUCT.md
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code of conduct.md file is missing it is generally present in good repos which have large community
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PR_kwDOIPDwls5dccJn
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Add Tavily Search API as a Tool
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CONTRIBUTOR
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Adding Tavily Search API as a tool. I will be the maintainer and assaf_elovic is the twitter handler.
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Retriever with score #11062
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NONE
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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Update index.ipynb
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| 2023-10-21T11:55:31
| 2023-10-23T17:45:31
| 2023-10-23T17:45:31
|
CONTRIBUTOR
| null |
Fixed some minor errors
|
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I_kwDOIPDwls50jdRQ
| 12,100
|
"GoogleVertexAISearchRetriever" object has no field "_serving_config". It's a Pydantic-related bug.
|
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[] | null | 2
| 2023-10-21T11:31:24
| 2023-10-24T08:20:04
| 2023-10-24T08:20:03
|
CONTRIBUTOR
| null |
### System Info
When upgrading from version 0.317 to 0.318, there is a bug regarding the Pydantic model validations. This issue doesn't happen in versions before 0.318.
File "...venv/lib/python3.10/site-packages/langchain/retrievers/google_vertex_ai_search.py", line 268, in __init__
self._serving_config = self._client.serving_config_path(
File "pydantic/main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "GoogleVertexAISearchRetriever" object has no field "_serving_config"
### Who can help?
I tagged both of you, @eyurtsev and @kreneskyp, regarding this version 0.318 change, in case it is related: https://github.com/langchain-ai/langchain/pull/11936.
Thanks for this amazing library!
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Execute the following code:
```
def main():
aiplatform.init(project=PROJECT_ID)
llm = VertexAI(model_name=MODEL, temperature=0.0)
retriever = GoogleVertexAISearchRetriever(
project_id=PROJECT_ID, search_engine_id=DATA_STORE_ID
)
search_query = "Who was the CEO of DeepMind in 2021?"
retrieval_qa = RetrievalQA.from_chain_type(
llm=llm, chain_type="stuff", retriever=retriever
)
answer = retrieval_qa.run(search_query)
print(answer)
if __name__ == "__main__":
main()
```
### Expected behavior
To output the answer from the model, as it does in versions before 0.318
|
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PR_kwDOIPDwls5dcXsC
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|
Fix typo in jsonformer_experimental.ipynb
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| 2023-10-21T11:21:51
| 2023-10-23T19:35:55
| 2023-10-23T19:35:55
|
CONTRIBUTOR
| null |
HuggingFace -> Hugging Face
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50jBlc
| 12,097
|
feature: Reddit API Tool
|
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| 2023-10-21T05:49:41
| 2023-12-03T17:29:57
| 2023-12-03T17:29:57
|
CONTRIBUTOR
| null |
### Feature request
Feature request to Integrate a new Langchain tool that summarizes reddit user interactions/discussion based on user input.
### Motivation
Reddit is a vast platform with numerous discussions and threads covering a wide range of topics. Users often seek information, insights, and opinions on specific subjects of interest within these discussions. However, navigating through these discussions and identifying the most valuable insights can be a time-consuming and sometimes overwhelming process.
What we aim to provide is a summarization toolkit that simplifies the experience of exploring Reddit. With this toolkit, users can input a topic or subject they are interested in, and it will not only aggregate relevant discussions (posts) from Reddit but also generate concise and coherent summaries of these discussions. This tool streamlines the process of distilling key information, popular opinions, and diverse perspectives from Reddit threads, making it easier for users to stay informed and engaged with the platform's wealth of content.
In summary, our Reddit summarization toolkit is designed to save users time and effort by delivering informative and easily digestible summaries of Reddit discussions, ultimately enhancing their experience in accessing and comprehending the vast amount of information and opinions present on the platform.
### Your contribution
We are a group of 4 looking to contribute to Langchain as a course project and will be creating a PR for this issue in mid/late November.
|
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| 12,096
|
How to run Semi_structured_and_multi_modal_RAG.ipynb
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| 2023-10-21T02:51:14
| 2023-10-21T02:56:19
| null |
CONTRIBUTOR
| null |
When running on colab, it has following error:
```
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data] Unzipping tokenizers/punkt.zip.
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data] /root/nltk_data...
[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-2-3092bb1466a5>](https://localhost:8080/#) in <cell line: 7>()
5
6 # Get elements
----> 7 raw_pdf_elements = partition_pdf(filename=path+"LLaVA.pdf",
8 # Using pdf format to find embedded image blocks
9 extract_images_in_pdf=True,
8 frames
[/usr/local/lib/python3.10/dist-packages/unstructured_inference/models/base.py](https://localhost:8080/#) in get_model(model_name, **kwargs)
70 else:
71 raise UnknownModelException(f"Unknown model type: {model_name}")
---> 72 model.initialize(**initialize_params)
73 models[model_name] = model
74 return model
TypeError: UnstructuredYoloXModel.initialize() got an unexpected keyword argument 'extract_images_in_pdf'
```
https://github.com/langchain-ai/langchain/blob/5dbe456aae755e3190c46316102e772dfcb6e148/cookbook/Semi_structured_and_multi_modal_RAG.ipynb#L103
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PR_kwDOIPDwls5dbkpV
| 12,095
|
Comment modification
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| 2023-10-21T01:49:13
| 2023-10-24T02:07:13
| 2023-10-23T19:03:38
|
CONTRIBUTOR
| null |
Comment modification for _default_params in class baidu_qianfan_endpoint.py
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I_kwDOIPDwls50iXwJ
| 12,094
|
Chat API Endpoints with RAG
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[] | null | 2
| 2023-10-21T00:44:36
| 2023-10-21T15:54:43
| null |
NONE
| null |
### Feature request
Cohere's upcoming (currently in BETA) Co.Chat + RAG API Endpoint will offer additional parameters such as "documents" and "connectors" that can be used for RAG. This is the major new feature of the Cohere Coral models (RAG)
See https://docs.cohere.com/reference/chat
GPT-4 (and the Other Chat Models following soon after) I am sure will have similar ability to offer RAG with its various new chat models.
Would the ability to add additional parameters to API chat endpoints (such as a document folder or a url etc...) be able to be supported via the Langchain Runnable Interface once these new Chat endpoints API's are GA?
This support would be needed for the external RAG for both the Input Type (_PromptValue?_) and the Output Type. (ChatMessage)
### Motivation
The main motivation for this proposal for updating the Input and Output Types in the Langchain Runnable Interface _(if that is indeed the correct place from a code standpoint this would be supported_) is to support RAG inputs and outputs (HumanMessage and AIMessage) with the newer Chat Models from Cohere, OpenAI etc. and in very near future support multimodal LLMs which are fast becoming the norm.
### Your contribution
I would need a bit of help with the PR.
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docstrings update
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| 2023-10-24T18:42:37
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COLLABORATOR
| null |
Added missed docstrings. Added missed Args:, Returns: Raises:
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PyPDFLoader use url in metadata source if file is a web path
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|
CONTRIBUTOR
| null |
**Description:** Update `langchain.document_loaders.pdf.PyPDFLoader` to store url in metadata (instead of a temporary file path) if user provides a web path to a pdf
- **Issue:** Related to #7034; the reporter on that issue submitted a PR updating `PyMuPDFParser` for this behavior, but it has unresolved merge issues as of 20 Oct 2023 #7077
- In addition to `PyPDFLoader` and `PyMuPDFParser`, these other classes in `langchain.document_loaders.pdf` exhibit similar behavior and could benefit from an update: `PyPDFium2Loader`, `PDFMinerLoader`, `PDFMinerPDFasHTMLLoader`, `PDFPlumberLoader` (I'm happy to contribute to some/all of that, including assisting with `PyMuPDFParser`, if my work is agreeable)
- The root cause is that the underlying pdf parser classes, e.g. `langchain.document_loaders.parsers.pdf.PyPDFParser`, never receive information about the url; the parsers receive a `langchain.document_loaders.blob_loaders.blob`, which contains the pdf contents and local file path, but not the url
- This update passes the web path directly to the parser since it's minimally invasive and doesn't require further changes to maintain existing behavior for local files... bigger picture, I'd consider extending `blob` so that extra information like this can be communicated, but that has much bigger implications on the codebase which I think warrants maintainer input
- **Dependencies:** None
```python
# old behavior
>>> from langchain.document_loaders import PyPDFLoader
>>> loader = PyPDFLoader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': '/var/folders/w2/zx77z1cs01s1thx5dhshkd58h3jtrv/T/tmpfgrorsi5/tmp.pdf', 'page': 0}
# new behavior
>>> from langchain.document_loaders import PyPDFLoader
>>> loader = PyPDFLoader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': 'https://arxiv.org/pdf/1706.03762.pdf', 'page': 0}
```
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Integrate MemGPT
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| 2023-10-20T20:33:10
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NONE
| null |
### Feature request
Implement MemGPT for better memory management with local and hosted models:
https://github.com/cpacker/MemGPT
### Motivation
This can help automate the manually creation and retrieval of vector stores.
### Your contribution
I'm not really familiar with the langchain repo at the moment, I could provide feedback on the implementation from a users perspective.
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Add CI check that integration tests compile
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PR_kwDOIPDwls5dakQX
| 12,089
|
Minor update to doc/text in IPYNB example
|
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| 2023-10-20T19:25:08
| 2023-10-20T21:17:37
| 2023-10-20T21:17:36
|
CONTRIBUTOR
| null |
- **Description:** changed sign-up link in IPYNB example
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ofermend
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I_kwDOIPDwls50hVcZ
| 12,087
|
Improve flexibility of ReAct agent on the first iteration
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| 2023-10-25T15:54:28
| 2023-10-25T15:54:27
|
NONE
| null |
### Feature request
Many of the non-OpenAI LLMs struggle to complete the first "Thought" part of the prompt. Falcon, Llama, Mistral, Zephyr, and many others will successfully choose the action and action input, but leave out an initial thought.
### Motivation
For the very first interaction, the agent scratchpad is empty, and the first thought isn't always necessarily helpful, especially if the first action and action input are valid.
Interestingly, removing the "thought" portion of the prompt often results in the LLM properly completing the thought. However, doing so breaks Langchain because Langchain is expecting the agent_scratchpad variable to be present in the template for completion.
An alternative could be to make the entire "Thought" part of the prompt only present if agent_scratchpad isn't empty. However, that would require "building" the prompt template conditionally.
### Your contribution
Most of this takes place in either the MRKL agent:
https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/agents/mrkl
Or potentially the ReAct Docstore agent:
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/react/base.py
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PR_kwDOIPDwls5daTyo
| 12,086
|
Add multiple models and streaming support to OpenAICallbackHandler
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| 2023-10-20T18:26:16
| 2023-12-20T15:24:34
| null |
CONTRIBUTOR
| null |
### Description
Old OpenAICalbackHandler does not support streaming mode, which is used widely today. Also, it is hard to differentiate between models when using the same callback with different models. It is particularly useful when used in agents combinig "smart" and "fast" models. This PR is an attempt to solve these problems.
If tiktoken is not installed, my solution will work as it previously did, except that it will show a warning if used in conjunction with streaming model.
Additionally it adds per-model methods to get prompt, completion and total tokens, as well as total cost.
If a user will try to use total_tokens, prompt_tokens, completion_tokens after using the callback with multiple models, a warning will be displayed.
### Issue
#3114 #4583
### Dependencies
N/A
### Tag maintainer
Not yet :)
### Twitter handle
N/A
|
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| 1,954,681,397
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PR_kwDOIPDwls5dZ0kF
| 12,084
|
fix: Add `_serving_config` property to `GoogleVertexAISearchRetriever`
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| 2023-10-20T16:44:21
| 2023-10-23T15:40:20
| 2023-10-20T19:16:42
|
CONTRIBUTOR
| null |
- Fixes error:
```
ValueError: "GoogleVertexAISearchRetriever" object has no field "_serving_config"
```
Introduced in #11736
@baskaryan, @eyurtsev, @hwchase17 if you could review and merge quickly, that would be appreciated :)
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PR_kwDOIPDwls5dZmvf
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Allow specifying custom input/output schemas for runnables with .with_types()
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| 2023-10-20T16:13:22
| 2023-10-22T16:26:49
| 2023-10-22T16:26:49
|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50gBaa
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Confluence document loader not handling pagination correctly anymore (when using Confluence Cloud)
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[] | null | 3
| 2023-10-20T15:41:10
| 2024-01-09T08:48:55
| null |
CONTRIBUTOR
| null |
### System Info
LangChain 0.0.317
atlassian-python-api 3.41.3
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When playing with documents from Confluence (Cloud hosting in my case), I have noticed that I am getting multiple copies of the same document in my vector store or from my Confluence retriever (custom implementation not in LangChain). In both cases, the vector store is populated with LangChain's Confluence document loader and my retriever is also using that same document loader. The problem happens in `ConfluenceLoader.paginate_request()` in the `while` loop until we reach `max_pages`. In summary, you will get copies of the same documents until you reach `max_pages`. For example, if your query (I'm using CQL in my use-case) returns 10 documents and `max_pages` is set to `200`, you will get 20 copies of each document. This also makes the search process much slower.
The previous pagination system of Confluence REST server was leveraging an index controlled by the `start` parameter. According to this [page](https://developer.atlassian.com/cloud/confluence/change-notice-moderize-search-rest-apis/), it has been deprecated in favor of a cursor system. It is now recommended to follow `_links.next` instead of relying on the `start` parameter and getting an empty result when documents to be returned have been exhausted.
This change is now in effect for the Cloud hosting of Confluence, not sure about private deployments. You can see for yourself by running a script that looks like this:
```python
from atlassian import Confluence
site = "templates" # Public site
confluence = Confluence(url=f"https://{site}.atlassian.net/")
cql='type=page AND text~"is"'
response_1 = confluence.cql(cql=cql, start=0)
print(response_1) # Easier to see in debug mode
next_start = len(response_1["results"])*1000
response_2 = confluence.cql(cql=cql, start=next_start)
print(response_2) # Notice returned documents are the same as in response_1
```
### Expected behavior
The Confluence document loader should handle properly pagination for both the old (using `start`) and new way (cursor) to maintain backward compatibility for private Confluence deployments. It should also properly stop when `max_pages` has been reached.
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PR_kwDOIPDwls5dZLFx
| 12,081
|
Fixed bug in PGVector database connection
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| 2023-10-20T15:11:36
| 2023-10-26T15:27:20
| null |
NONE
| null |
Description: This change turns on futures in the PGVector connection. With futures turned on, commits are enabled.
Issue: A bug occurs when you try to create an empty PGVector vector store, rather than creating it from_documents which occurs much more frequently. Under these conditions, you will not be able to commit changes to the database, leading to errors as soon as this is tried.
Dependencies: None
Tag maintainer: @hwchase17
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|
Add neo4j graph environment variables
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CONTRIBUTOR
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I_kwDOIPDwls50fLbq
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|
Issue: TokenTextSplitter with local tokenizer ?
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[] | null | 4
| 2023-10-20T13:46:35
| 2023-10-20T14:30:20
| 2023-10-20T14:30:19
|
NONE
| null |
### Issue you'd like to raise.
Hello,
I am using langchain offline on a local machine. I'd like to split document over tokens user TokenTextSplitter.
Unfortunately, I can't get to make the class to user a local tokenizer.
I tried to do
`text_splitter = TokenTextSplitter(model_name='/my/path/to/my/tokenizer/', chunk_size = 50, chunk_overlap = 10`
like I did for the HuggingFaceEmbedding (and it worked pretty well).
But I get the following error:
Could not automaticcaly map '/my/path/to/my/tokenizer/' to a tokenizer. Please use 'tiktoken.get_encoding' to explicitly get the tokenizer you expect
Couldn't find any info in the documentation about setup an offline / local tokenizer.
### Suggestion:
_No response_
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I_kwDOIPDwls50e881
| 12,077
|
Running SQLDatabaseChain adds prefix "SQLQuery:\n" infront of returned SQL by LLM, causing invalid query when ran on Database using chain
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[] | null | 10
| 2023-10-20T13:14:25
| 2024-01-07T16:32:38
| null |
NONE
| null |
### System Info
Running SQLDatabaseChain with LangChain version 0.0.319 and Snowflake return SQL query which is to be executed on snowflake database in next step. But returned query contains the prefix "SQLQuery:\n" which will broke the whole chain when the query gets executed on snowflake. How to get rid of this "SQLQuery:\n" prefix from query.
Note: Using AWS Bedrock endpoint with Anthropic Claudev2 LLM.
Using default prompt provided in the langchain:
`Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: Question here
SQLQuery: SQL Query to run
SQLResult: Result of the SQLQuery
Answer: Final answer here
`
Getting following response in debug/verbose mode:
`"text": " SQLQuery:\nSELECT top 5 p.productname, sum(od.quantity) as total_sold\nFROM products p\nJOIN orderdetails od ON p.productid = od.productid \nGROUP BY p.productname\nORDER BY total_sold DESC\n"
}`
Any help would be appreciated.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just use SQLDatabaseChain with AWS Bedrock Anthropic Claude 2 to produce this one.
### Expected behavior
Should not prefix "SQLQuery:\n" in front returned SQL query.
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I_kwDOIPDwls50exMc
| 12,076
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Function get_relevant_docs() returning AttributeError
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open
| false
| null |
[] | null | 3
| 2023-10-20T12:49:04
| 2023-11-15T11:46:26
| null |
NONE
| null |
### System Info
MacOS Ventura 13.6
Python 3.10.13
langchain 0.0.306
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
llm = AzureOpenAI(temperature=0, model="gpt-35-turbo")
compressor = LLMChainExtractor.from_llm(llm)
base_retriever = vectorstores.as_retriever()
compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=base_retriever)
compression_retriever.get_relevant_documents("Owner")
```
### Expected behavior
Expected to return the docs compressed from the vectorstore, but I'm getting the `AttributeError: 'str' object has no attribute 'get'`
|
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PR_kwDOIPDwls5dYLkg
| 12,075
|
Fix typos in whatsapp.ipynb and telegram.ipynb
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[] | null | 1
| 2023-10-20T12:36:29
| 2023-10-20T22:45:34
| 2023-10-20T22:45:33
|
CONTRIBUTOR
| null |
- **Description:**
- Replace Telegram with Whatsapp in whatsapp.ipynb
- Add # to mark the telegram as heading in telegram.ipynb
- **Issue:** None
- **Dependencies:** None
|
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I_kwDOIPDwls50eqSr
| 12,074
|
DOC: Deprecated Import Statement in Documentation for OpenAI
|
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| 2023-10-20T12:31:33
| 2023-10-20T12:33:41
| null |
NONE
| null |
### Issue with current documentation:
#### **Description**
In various places within the documentation, the import statement is being used:
```python
from langchain.llms import OpenAI
```
However, as hinted in https://github.com/langchain-ai/langchain/commit/779790167e37f49b3eec5d04dfd30b0447d4a32a, this statement has been deprecated. The correct and updated import statement should be:
```python
from langchain.chat_models import ChatOpenAI
```
#### **Problem**
The use of the deprecated import statement leads to problems, particularly when interfacing with Pydantic. Users following the outdated documentation might experience unexpected errors or issues due to this.
|
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PR_kwDOIPDwls5dX9zI
| 12,073
|
Add multiple models and streaming support to OpenAICallbackHandler
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| 2023-10-20T11:59:57
| 2023-10-20T18:26:24
| 2023-10-20T18:16:37
|
CONTRIBUTOR
| null |
### Description
Old OpenAICalbackHandler does not support streaming mode, which is used widely today. Also, it is hard to differentiate between models when using the same callback with different models. It is particularly useful when used in agents combinig "smart" and "fast" models. This PR is an attempt to solve these problems.
If tiktoken is not installed, my solution will work as it previously did, except that it will show a warning if used in conjunction with streaming model.
Additionally it adds per-model methods to get prompt, completion and total tokens, as well as total cost.
If a user will try to use total_tokens, prompt_tokens, completion_tokens after using the callback with multiple models, a warning will be displayed.
### Issue
#3114 #4583
### Dependencies
N/A
### Tag maintainer
Not yet :)
### Twitter handle
N/A
|
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I_kwDOIPDwls50ePDg
| 12,072
|
system_message in agent_kwargs not updating System Message in AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION
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[] | null | 3
| 2023-10-20T11:21:19
| 2023-11-06T08:27:49
| 2023-11-06T08:15:34
|
NONE
| null |
### System Info
Langchain Version: 0.0.316 - Python 3.8.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
@hwchase17 @agola11
I am using StructuredTool for multi input support. Below is my initialize_agent
```
sys_msg = "Assistant’s main duty is to decide which tool to use......"
system_message = SystemMessage(content=sys_msg)
agent_executor = initialize_agent(
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
tools=tools,
llm=llm,
verbose=True,
max_iterations=3,
early_stopping_method='generate',
handle_parsing_errors=True,
agent_kwargs = { "system_message": system_message }
)
```
I dont see the above (sys_msg) as the system_message in the openAI query. Instead I see this in the debug logs
```
2023-10-20 08:54:01 DEBUG api_version=None data='{"messages": [{"role": "system", "content": "Respond to the human as helpfully and accurately as possible. You have access to the following tools:\\n\\n
```
I believe this is the default system message. How do I change this to my custom system message in AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION.
I see a similar ticket for OpenAI functions AgentType : https://github.com/langchain-ai/langchain/issues/6334
I also tried specifying system_message=system_message as discussed in the above ticket. That did not help either
### Expected behavior
The custom system_message that I provide in agent_kwargs should replace the default one going out to openAI.
|
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| 12,071
|
AttributeError: 'OpenAIEmbeddings' object has no attribute 'skip_empty'
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| 2023-10-20T11:01:54
| 2023-10-20T11:08:36
| null |
NONE
| null |
### System Info
Platform: local development on MacOS Ventura
Python version: 3.9.7
langchain.version: 0.0.315
faiss.version: 1.7.4
openai.version: 0.28.1
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] 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
This is my code:
class TestGPT(object):
def __init__(self):
self.llm_model = ChatOpenAI(temperature=0.1, streaming=True, model="gpt-3.5-turbo")
self.store = self.load_store_data()
self.prompt = f"""Use 3 sentences at most."""
@staticmethod
def load_store_data():
with open(f"faiss_store.pkl", "rb") as f:
store = pickle.load(f)
store.index = faiss.read_index(f"docs.index")
return store
def ask(self, user_prompt):
content = self.prompt + "\n\n" + "Question: " + user_prompt + "\n\n"
chain = RetrievalQAWithSourcesChain.from_chain_type(llm=self.llm_model,
retriever=self.store.as_retriever())
gpt_response = chain(content)['answer']
return self.control_response_has_source(gpt_response)
TestGPT().ask("hello")
Error is **"AttributeError: 'OpenAIEmbeddings' object has no attribute 'skip_empty'"**
Traceback:
`Traceback (most recent call last):
File "/Users/md/Desktop/support_gpt.py", line 72, in <module>
res = SupportGPT().ask("uygulama nasıl kullanılır")
File "/Users/md/Desktop/support_gpt.py", line 45, in ask
gpt_response = chain(content)['answer']
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/chains/base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/chains/qa_with_sources/base.py", line 151, in _call
docs = self._get_docs(inputs, run_manager=_run_manager)
File "/Users/md/Desktop/venv/venv/lib/python3.9/site-packages/langchain/chains/qa_with_sources/retrieval.py", line 50, in _get_docs
docs = self.retriever.get_relevant_documents(
File "/Users/md/Desktop/venv/venv/lib/python3.9/site-packages/langchain/schema/retriever.py", line 211, in get_relevant_documents
raise e
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/schema/retriever.py", line 204, in get_relevant_documents
result = self._get_relevant_documents(
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/schema/vectorstore.py", line 585, in _get_relevant_documents
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 364, in similarity_search
docs_and_scores = self.similarity_search_with_score(
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 305, in similarity_search_with_score
embedding = self._embed_query(query)
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 138, in _embed_query
return self.embedding_function(text)
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 518, in embed_query
return self.embed_documents([text])[0]
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 490, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 374, in _get_len_safe_embeddings
response = embed_with_retry(
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 107, in embed_with_retry
return _embed_with_retry(**kwargs)
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "/Users/md/Desktop/venv/python3.9/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py", line 438, in result
return self.__get_result()
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py", line 390, in __get_result
raise self._exception
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "/Users/md/Desktop/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 105, in _embed_with_retry
return _check_response(response, skip_empty=embeddings.skip_empty)
AttributeError: 'OpenAIEmbeddings' object has no attribute 'skip_empty'
`
### Expected behavior
This function should not throw an error and also I do not get any error when I downgrade the langchain to 0.0.251
|
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| 1,954,053,019
|
PR_kwDOIPDwls5dXqz6
| 12,070
|
Takeoff pro support
|
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closed
| false
| null |
[] | null | 2
| 2023-10-20T11:00:43
| 2023-10-26T16:39:33
| 2023-10-26T16:39:32
|
CONTRIBUTOR
| null |
**Description:**
This PR adds support for the [Pro version of Titan Takeoff Server](https://docs.titanml.co/docs/category/pro-features). Users of the Pro version will have to import the TitanTakeoffPro model, which is different from TitanTakeoff.
**Issue:**
Also minor fixes to docs for Titan Takeoff (Community version)
**Dependencies:**
No additional dependencies
**Twitter handle:** @becoming_blake
@baskaryan @hwchase17
|
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| 1,953,813,752
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I_kwDOIPDwls50dNT4
| 12,069
|
Support other embedder in Hugginface Hub
|
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open
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[] | null | 0
| 2023-10-20T08:39:34
| 2023-10-20T08:41:43
| null |
NONE
| null |
### Feature request
At the moment huggingface_hub.py supports only sentence-transformers because there is a validation:
```
if not repo_id.startswith("sentence-transformers"):
raise ValueError(
"Currently only 'sentence-transformers' embedding models "
f"are supported. Got invalid 'repo_id' {repo_id}."
)
```
### Motivation
At the moment there are other higher-performing embedders on the hub, like e5 or bge family.
### Your contribution
I think you should relax the constraint by also including embedders supported by sentence-transformer
```
if not repo_id.startswith(("sentence-transformers", "intfloat", "BAAI")):
raise ValueError(
"Currently only 'sentence-transformers' embedding models "
f"are supported. Got invalid 'repo_id' {repo_id}."
)
```
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I_kwDOIPDwls50dIlz
| 12,068
|
feat: Add Linearized output to Textract PDFLoader
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[] | null | 1
| 2023-10-20T08:28:07
| 2023-10-31T01:02:11
| 2023-10-31T01:02:11
|
CONTRIBUTOR
| null |
### Feature request
Textract released the [LAYOUT](https://docs.aws.amazon.com/textract/latest/dg/layoutresponse.html) feature, which identifies different layout elements like tables, lists, figures, text-paragraphs and titles. This should be used by the AmazonTextractPDFParser to generate a linearized output to improve downstream LLMs accuracy with those hints.
Text output should render tables and key/value pairs and text in reading order for multi-column text and prefix lists with a *, when features like LAYOUT, TABLES, FORMS are passed to the textract call
### Motivation
Improve downstream LLM accuracy
### Your contribution
I'll submit a PR for this feature.
|
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| 1,953,754,290
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I_kwDOIPDwls50c-yy
| 12,067
|
DOC: Improve MarkDown Splitting and Metadata as Part of RAG.
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[] | null | 2
| 2023-10-20T08:02:52
| 2023-12-07T19:41:07
| null |
NONE
| null |
### Issue with current documentation:
The current documentation for rag does not focus on adding metadata to chunks to make sure before even doing similarity search on vector db only the relevant docs with correct metadata are retrieved from vector db.
The issue is with open source embedding models having only mostly 512 sequence length.
Lot of context is lost if correct metadata is not present in each chunk.
This improves ability of local model to improve results by factor of magnitude. We tested the same with local hosted embedded model and codellama to improve queries on mermaid and currently getting results onpar with gpt4.
### Idea or request for content:
This notebook covers the same.
https://github.com/unoplat/unoplat-lamp/blob/1-mermaid-expert/llm-rag/Mermaid%20Expert%20RAG.ipynb
The comparison is done with codellama without the KB and gpt4.
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PR_kwDOIPDwls5dWXiv
| 12,066
|
Added new feature (new classes) to parse and load pdfs using PyMuPDF from bytes buffer
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| 2023-11-05T11:29:05
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NONE
| null |
Hi, here is the required info for this pull request.
Description:
I've added a new feature (new classes) to parse and load pdfs using PyMuPDF from bytes buffer. This is important when the PDF is not a file but is a bytes buffer in memory. Especially, it's useful when working and using Langchain with Spark and Databricks Autoloader.
I've also added a unit test.
Dependencies:
No new dependencies is required. Just io library which is in the native python packaging.
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PR_kwDOIPDwls5dWWaM
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Support runnable factories in .configurable_alts()
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<!-- Thank you for contributing to LangChain!
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See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5dVw7o
| 12,063
|
Fix typo in stuff_prompt's system_template
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| 2023-10-24T16:21:29
| 2023-10-24T16:21:29
|
CONTRIBUTOR
| null |
- **Description:**
Add missing apostrophe in `user's` in stuff_prompt's system_template. The first sentence in the system template went from:
> Use the following pieces of context to answer the users question.
to
> Use the following pieces of context to answer the user's question.
- **Issue:**
- **Dependencies:** none
- **Tag maintainer:** @baskaryan
- **Twitter handle:** ojohnnyo
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See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5dVqVp
| 12,062
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Fix superfluous Auto-fixing parser documents
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CONTRIBUTOR
| null |
Replace this entire comment with:
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- **Dependencies:** N/A,
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- **Twitter handle:** @dosuken123
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See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls50bsWU
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|
qdrant.py doesn't contain any allowed_operators
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NONE
| null |
### System Info
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aiohttp==3.8.6
aiosignal==1.3.1
altair==5.1.2
annotated-types==0.6.0
anyio==3.7.1
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asgiref==3.7.2
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async-timeout==4.0.3
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auto-gptq==0.4.2
backcall==0.2.0
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bitsandbytes==0.41.1
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build==1.0.3
cachetools==5.3.1
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certifi==2023.7.22
cffi==1.16.0
chardet==5.2.0
charset-normalizer==3.3.0
circus==0.18.0
click==8.1.7
click-option-group==0.5.6
cloudpickle==3.0.0
cmake==3.27.7
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cryptography==41.0.4
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cuda-python==12.2.0
cycler==0.12.1
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dataclasses-json==0.6.1
datasets==2.14.5
debugpy==1.8.0
decorator==5.1.1
deepmerge==1.1.0
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dill==0.3.7
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executing==2.0.0
fastcore==1.5.29
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GitPython==3.1.40
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grpcio-health-checking==1.59.0
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humanfriendly==10.0
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InstructorEmbedding==1.0.1
ipykernel==6.25.2
ipython==8.16.1
ipywidgets==8.1.1
jedi==0.19.1
Jinja2==3.1.2
joblib==1.3.2
JPype1==1.4.1
jsonpatch==1.33
jsonpointer==2.4
jsonschema==4.19.1
jsonschema-specifications==2023.7.1
jupyter_client==8.4.0
jupyter_core==5.4.0
jupyterlab-widgets==3.0.9
kiwisolver==1.4.5
langchain==0.0.318
langsmith==0.0.47
lark==1.1.7
lxml==4.9.3
markdown-it-py==3.0.0
MarkupSafe==2.1.3
marshmallow==3.20.1
matplotlib==3.8.0
matplotlib-inline==0.1.6
mdurl==0.1.2
mpmath==1.3.0
multidict==6.0.4
multiprocess==0.70.15
mypy-extensions==1.0.0
nest-asyncio==1.5.8
networkx==3.2
ninja==1.11.1.1
nltk==3.8.1
numexpr==2.8.7
numpy==1.26.1
openai==0.28.1
openapi-schema-pydantic==1.2.4
openllm==0.3.9
openllm-client==0.3.9
openllm-core==0.3.9
openpyxl==3.1.2
opentelemetry-api==1.20.0
opentelemetry-instrumentation==0.41b0
opentelemetry-instrumentation-aiohttp-client==0.41b0
opentelemetry-instrumentation-asgi==0.41b0
opentelemetry-instrumentation-grpc==0.41b0
opentelemetry-sdk==1.20.0
opentelemetry-semantic-conventions==0.41b0
opentelemetry-util-http==0.41b0
optimum==1.13.2
orjson==3.9.9
packaging==23.2
pandas==2.1.1
parso==0.8.3
pathspec==0.11.2
pdfminer.six==20221105
pdfquery==0.4.3
peft==0.5.0
pickleshare==0.7.5
Pillow==10.1.0
pip-autoremove==0.10.0
pip-requirements-parser==32.0.1
pip-review==1.3.0
pip-tools==7.3.0
platformdirs==3.11.0
portalocker==2.8.2
prometheus-client==0.17.1
prompt-toolkit==3.0.39
protobuf==4.24.4
psutil==5.9.6
pure-eval==0.2.2
pyarrow==13.0.0
pycparser==2.21
pycryptodome==3.19.0
pydantic==2.4.2
pydantic_core==2.10.1
pydeck==0.8.0
Pygments==2.16.1
Pympler==1.0.1
PyMuPDF==1.23.5
pymupdf-fonts==1.0.5
PyMuPDFb==1.23.5
pynvml==11.5.0
pyparsing==3.1.1
pyproject_hooks==1.0.0
pyquery==2.0.0
pyreadline3==3.4.1
python-dateutil==2.8.2
python-dotenv==1.0.0
python-json-logger==2.0.7
python-multipart==0.0.6
pytz==2023.3.post1
pytz-deprecation-shim==0.1.0.post0
pywin32==306
PyYAML==6.0.1
pyzmq==25.1.1
qdrant-client==1.6.3
referencing==0.30.2
regex==2023.10.3
requests==2.31.0
rich==13.6.0
roman==4.1
rouge==1.0.1
rpds-py==0.10.6
safetensors==0.4.0
schema==0.7.5
scikit-learn==1.3.1
scipy==1.11.3
sentence-transformers==2.2.2
sentencepiece==0.1.99
sigfig==1.3.3
simple-di==0.1.5
six==1.16.0
smmap==5.0.1
sniffio==1.3.0
sortedcontainers==2.4.0
spyder-kernels==2.4.4
SQLAlchemy==2.0.22
stack-data==0.6.3
starlette==0.31.1
streamlit==1.27.2
streamlit-chat==0.1.1
sympy==1.12
tabula-py==2.8.2
tabulate==0.9.0
tenacity==8.2.3
threadpoolctl==3.2.0
tiktoken==0.5.1
tokenizers==0.14.1
toml==0.10.2
toolz==0.12.0
torch==2.1.0
torchaudio==2.1.0
torchvision==0.16.0
tornado==6.3.3
tqdm==4.66.1
traitlets==5.11.2
transformers @ git+https://github.com/huggingface/transformers@43bfd093e1817c0333a1e10fcbdd54f1032baad0
typing-inspect==0.9.0
typing_extensions==4.8.0
tzdata==2023.3
tzlocal==5.1
urllib3==1.26.18
uvicorn==0.23.2
validators==0.22.0
watchdog==3.0.0
watchfiles==0.21.0
wcwidth==0.2.8
widgetsnbextension==4.0.9
wrapt==1.15.0
xformers==0.0.22.post4
xlrd==2.0.1
xxhash==3.4.1
yarl==1.9.2
zipp==3.17.0
using python 3.11
### Who can help?
_No response_
### Information
- [ ] 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from dotenv import load_dotenv
import os
from langchain.chat_models import ChatOpenAI
from qdrant_client import QdrantClient as qcqc
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import Qdrant
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
load_dotenv()
openai_key = os.getenv('OPENAI_API_KEY')
db_path = os.getenv('vectordb_local_path')
key = openai_key
llm = ChatOpenAI(
temperature = 0,
model = 'gpt-3.5-turbo',
streaming = True)
text_metadata = [AttributeInfo(name = 'book name',
description = "name of the book.",
type = "string"),
AttributeInfo(name = 'author',
description = 'Author of the book',
type = 'string'),
AttributeInfo(name = 'creation data',
description = 'the date the book was written',
type = 'list[int]'),
AttributeInfo(name = 'page',
description = "page number.",
type = "int"),
AttributeInfo(name = 'images',
description = "dictionary whoes keys are name and description of images on the page,\
and whoes contents are image references on pdfs",
type = "dict{string:string}"),
AttributeInfo(name = 'tables',
description = 'list of tables from the page',
type = 'list[dataframe]')
]
def retreive_conversation_construct(store,store_content_description, metadata_format=text_metadata,verbose=False):
''' this is the first part of this function, and is the first problem i ran into
'''
retriever = SelfQueryRetriever.from_llm(llm = llm,
vectorstore=store,
document_contents = store_content_description,
metadata_field_info = metadata_format,
enable_limit=True,
fix_invalid = True,
verbose=verbose)
return retriever
client = qcqc(path= db_path)
model_name = "hkunlp/instructor-xl"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True}
load_dotenv()
path = os.getenv('instructor_local_dir')
os.environ['CURL_CA_BUNDLE'] = ''
embed_instruction ='Represent the document for retrieval: '
embeddings = HuggingFaceInstructEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
cache_folder = path,
embed_instruction = embed_instruction)
vector_store = Qdrant(client= client, collection_name= 'my cluster', embeddings= embeddings)
store_content_description = 'this is a paper about generating training data for large language models.'
retreive_conversation_construct(vector_store,store_content_description)
### Expected behavior
retriever should get generated.
I found in self_query.py, the .from_llm() method eventually leads to _get_builtin_translator getting called, which returns QdrantTranslator(metadata_key=vectorstore.metadata_payload_key) as structured_query_translator.
but later when calling structured_query_translator.allowed_operators from qdrant.py, the QdrantTranslator doesn't have allowed_operators, thus returns a None object.
this results in the following error:
File d:\ai_dev\research_assistant\testing.py:74
retreive_conversation_construct(vector_store,store_content_description)
File d:\ai_dev\research_assistant\testing.py:50 in retreive_conversation_construct
retriever = SelfQueryRetriever.from_llm(llm = llm,
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\retrievers\self_query\base.py:214 in from_llm
query_constructor = load_query_constructor_runnable(
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\query_constructor\base.py:317 in load_query_constructor_runnable
prompt = get_query_constructor_prompt(
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\query_constructor\base.py:203 in get_query_constructor_prompt
allowed_operators=" | ".join(allowed_operators),
TypeError: can only join an iterable
|
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| 1,953,390,150
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PR_kwDOIPDwls5dVa9Q
| 12,060
|
Update figures in multi-modal Cookbooks
|
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COLLABORATOR
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PR_kwDOIPDwls5dVaGi
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Baichuan & Hunyuan set default api_base
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CONTRIBUTOR
| null |
### Description
Baichuan & Hunyuan set default api_base env
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PR_kwDOIPDwls5dVMZY
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Shadowfaxproject patch 1
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NONE
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls50bA_t
| 12,057
|
The LCEL memory example returns KeyError
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### System Info
langchain==0.0.319, mac, python 3.10
### Who can help?
@hwchase17 @agola11
I'm trying to use this exact example from: https://python.langchain.com/docs/expression_language/cookbook/memory
```
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful chatbot"),
MessagesPlaceholder(variable_name="history"),
("human", "{input}")
])
memory = ConversationBufferMemory(return_messages=True)
memory.load_memory_variables({})
chain = RunnablePassthrough.assign(
memory=RunnableLambda(memory.load_memory_variables) | itemgetter("history")
) | prompt | model
inputs = {"input": "hi im bob"}
response = chain.invoke(inputs)
```
and getting:
```
File "/Users/name/.pyenv/versions/3.10.10/lib/python3.10/site-packages/langchain/schema/prompt_template.py", line 60, in <dictcomp>
**{key: inner_input[key] for key in self.input_variables}
KeyError: 'history'
```
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Please run the code above.
### Expected behavior
To have it work like the documentation.
|
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PR_kwDOIPDwls5dUxBI
| 12,056
|
Fix typo
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CONTRIBUTOR
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PR_kwDOIPDwls5dUh1D
| 12,055
|
clear nb img output
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COLLABORATOR
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I_kwDOIPDwls50aXQe
| 12,054
|
Add Async _acall and _astream to Bedrock
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[] | null | 2
| 2023-10-19T21:16:32
| 2023-10-19T23:47:24
| null |
NONE
| null |
### Feature request
Currently Bedrock and BedrockChat models do not sypport async calls and streaming.
it would be very useful to have working ChatOpenAI methods like _acall and _astream in Bedrock llms too so that we can use Claude2 and other Bedrock Models in production easily
### Motivation
without async functionalities it is hard to build production level chatbots using Bedrock models, including claude2 which is one of the most desired models
### Your contribution
I am trying myself, but I am having difficulties. If someone can help, it would be very appreciated by many,
|
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PR_kwDOIPDwls5dUUDt
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Bagatur/mv singlestore doc
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PR_kwDOIPDwls5dUTNh
| 12,052
|
Support Fireworks batching (#8)
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|
CONTRIBUTOR
| null |
Description
* Add _generate and _agenerate to support Fireworks batching.
* Add stop words test cases
* Opt out retry mechanism
Issue - Not applicable
Dependencies - None
Tag maintainer - @baskaryan
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PR_kwDOIPDwls5dUPYb
| 12,051
|
optional reusable connection
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| 2023-10-19T20:58:37
| 2023-10-27T18:52:43
| 2023-10-27T18:52:42
|
CONTRIBUTOR
| null |
My postgres out of connections after continuous PGVector usage, and the reason because it constantly creates new connections, so adding a reusable pre established connection seems like solves an issue
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I_kwDOIPDwls50aRGU
| 12,050
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Issue: description for the custom tool's output
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| 2023-10-19T20:56:28
| 2023-10-19T20:58:35
| null |
NONE
| null |
### Issue you'd like to raise.
In the [documentation](https://python.langchain.com/docs/modules/agents/tools/custom_tools), it's mentioned that expected input parameters could be defined though `args_schema` for the custom tool:
```
class CalculatorInput(BaseModel):
question: str = Field()
class CustomCalculatorTool(BaseTool):
name = "Calculator"
description = "useful for when you need to answer questions about math"
args_schema: Type[BaseModel] = CalculatorInput
```
The output of my custom function is a complex table with multiple columns and diverse data types. I would like to provide clearer descriptions for each column, including the possible values that can be found in each column. I assume that this way, the agent can utilize the data more effectively.
Can you please clarify if it is possible to describe the output from the custom tool in a similar manner as `args_schema` for the agent?
### Suggestion:
Would something like this work for the output description? (Of course, the need for such a solution would be for more complex outputs.)
```
class CalculatorInput(BaseModel):
question: str = Field()
class CalculatorOutput(BaseModel):
answer: str = Field()
class CustomCalculatorTool(BaseTool):
name = "Calculator"
description = "useful for when you need to answer questions about math"
args_schema: Type[BaseModel] = CalculatorInput
output_schema: Type[BaseModel] = CalculatorOutput
```
Thank you!`
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I_kwDOIPDwls50aM9p
| 12,049
|
Toy vectordb embedding example adopted to llama-cpp-python causes failure
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NONE
| null |
### System Info
I filed an issue with llama-cpp here https://github.com/ggerganov/llama.cpp/issues/3689
langchain
```Name: langchain
Version: 0.0.208
Summary: Building applications with LLMs through composability
Home-page: https://www.github.com/hwchase17/langchain
Author:
Author-email:
License: MIT
Location: Work\SHARK\shark.venv\Lib\site-packages
Requires: aiohttp, dataclasses-json, langchainplus-sdk, numexpr, numpy, openapi-schema-pydantic, pydantic, PyYAML, requests, SQLAlchemy, tenacity
```
llama-cpp-python
```Name: llama_cpp_python
Version: 0.2.11
Summary: Python bindings for the llama.cpp library
Home-page:
Author:
Author-email: Andrei Betlen <abetlen@gmail.com>
License: MIT
Location: Work\SHARK\shark.venv\Lib\site-packages
Requires: diskcache, numpy, typing-extensions
Required-by:
```
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
the toy code is adopted from https://learn.activeloop.ai/courses/take/langchain/multimedia/46317643-langchain-101-from-zero-to-hero
It's the first toy vector db embedding example with "Napoleon"
Here is the code to reproduce the error:
```
import os
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.embeddings import LlamaCppEmbeddings
from langchain.vectorstores import DeepLake
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import LlamaCpp
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# instantiate the LLM and embeddings models
llm = LlamaCpp(model_path="llama-2-13b-chat.Q5_K_M.gguf",
temperature=0,
max_tokens=1000, # this was lowered from the original value of 2000, but did not fix it
top_p=1,
Verbose=True)
embeddings = LlamaCppEmbeddings(model_path="llama-2-13b-chat.Q5_K_M.gguf")
# create our documents
texts = [
"Napoleon Bonaparte was born in 15 August 1769",
"Louis XIV was born in 5 September 1638"
]
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.create_documents(texts)
# create Deep Lake dataset
# TODO: use your organization id here. (by default, org id is your username)
my_activeloop_org_id = "<SOME_ID>"
my_activeloop_dataset_name = "langchain_llama_00"
dataset_path = f"hub://{my_activeloop_org_id}/{my_activeloop_dataset_name}"
db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings)
# add documents to our Deep Lake dataset
db.add_documents(docs)
retrieval_qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db.as_retriever())
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
tools = [
Tool(
name="Retrieval QA System",
func=retrieval_qa.run,
description="Useful for answering questions."
),
]
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
response = agent.run("When was Napoleone born?")
print(response)
```
in `agent.run(..)` line llama-cpp says it's running out of memory
```
ggml_allocr_alloc: not enough space in the buffer (needed 442368, largest block available 290848)
GGML_ASSERT: C:\Users\jason\AppData\Local\Temp\pip-install-4x0xr_93\llama-cpp-python_fec9a526add744f5b2436cab2e2c4c28\vendor\llama.cpp\ggml-alloc.c:173: !"not enough space in the buffer"
```
I don't know enough about how LlamaCppEmbeddings works to know if this is an error on my end, or a bug in llama-cpp.
Any guidance is appreciated.
Thank you
### Expected behavior
I expect it to work like the openai example!
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| 12,047
|
Fix documentation typo in Clickhouse Class
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|
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
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- **Description:** The return info in the documentation for similarity_search_by_vector and similarity_search_with_relevance_scores is wrong
|
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PR_kwDOIPDwls5dTtLu
| 12,046
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Always add run ID
|
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| 2023-10-19T19:38:08
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COLLABORATOR
| null |
in eval callback handler.
Useful if you're using a custom run evaluator and don't want to thread things through.
|
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PR_kwDOIPDwls5dTrgf
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Add zapier deprecation warning
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COLLABORATOR
| null |
Add zapier deprecation
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Merriam-Webster Dictionary Tool
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| 2023-10-19T18:47:51
| 2023-11-30T01:29:27
| 2023-11-30T01:28:29
|
CONTRIBUTOR
| null |
# Description
We implemented a simple tool for accessing the Merriam-Webster Collegiate Dictionary API (https://dictionaryapi.com/products/api-collegiate-dictionary).
Here's a simple usage example:
```py
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
llm = OpenAI()
tools = load_tools(["serpapi", "merriam-webster"], llm=llm) # Serp API gives our agent access to Google
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What is the english word for the german word Himbeere? Define that word.")
```
Sample output:
```
> Entering new AgentExecutor chain...
I need to find the english word for Himbeere and then get the definition of that word.
Action: Search
Action Input: "English word for Himbeere"
Observation: {'type': 'translation_result'}
Thought: Now I have the english word, I can look up the definition.
Action: MerriamWebster
Action Input: raspberry
Observation: Definitions of 'raspberry':
1. rasp-ber-ry, noun: any of various usually black or red edible berries that are aggregate fruits consisting of numerous small drupes on a fleshy receptacle and that are usually rounder and smaller than the closely related blackberries
2. rasp-ber-ry, noun: a perennial plant (genus Rubus) of the rose family that bears raspberries
3. rasp-ber-ry, noun: a sound of contempt made by protruding the tongue between the lips and expelling air forcibly to produce a vibration; broadly : an expression of disapproval or contempt
4. black raspberry, noun: a raspberry (Rubus occidentalis) of eastern North America that has a purplish-black fruit and is the source of several cultivated varieties —called also blackcap
Thought: I now know the final answer.
Final Answer: Raspberry is an english word for Himbeere and it is defined as any of various usually black or red edible berries that are aggregate fruits consisting of numerous small drupes on a fleshy receptacle and that are usually rounder and smaller than the closely related blackberries.
> Finished chain.
```
# Issue
This closes #12039.
# Dependencies
We added no extra dependencies.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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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:
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5dTmkP
| 12,043
|
add cookbook table
|
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PR_kwDOIPDwls5dTk8E
| 12,042
|
update `interface` notebook
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| 2023-10-19T22:22:21
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|
COLLABORATOR
| null |
Added a use case with parallelise on batches.
Simplified text.
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bump 319
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PR_kwDOIPDwls5dTjEZ
| 12,040
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More security notes
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[] | null | 1
| 2023-10-19T18:34:38
| 2023-10-19T18:49:10
| 2023-10-19T18:49:09
|
COLLABORATOR
| null |
Add more security notes
|
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| 1,952,840,501
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I_kwDOIPDwls50Zfs1
| 12,039
|
Tools for Dictionary APIs
|
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[] | null | 1
| 2023-10-19T18:31:45
| 2023-11-30T01:28:30
| 2023-11-30T01:28:30
|
CONTRIBUTOR
| null |
### Feature request
It would be nice to have agents that could access dictionary APIs such as the Merriam-Webster API or Urban Dictionary API (for slang).
### Motivation
It can be useful to be able to look up definitions for words using a dictionary to provide additional context. With no current dictionary tools available, it would be beneficial for there to be an implemented dictionary tool available at all.
### Your contribution
We will open a PR that adds a new tool for accessing the Merriam-Webster Collegiate Dictionary API (https://dictionaryapi.com/products/api-collegiate-dictionary[/](https://www.dictionaryapi.com/)), which provides definitions for English words, as soon as possible. In the future this could be extended to support other Merriam-Webster APIs such as their Medical Dictionary API (https://dictionaryapi.com/products/api-medical-dictionary) or Spanish-English Dictionary API (https://dictionaryapi.com/products/api-spanish-dictionary).
We may also open another PR for Urban Dictionary API integration.
|
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PR_kwDOIPDwls5dThR5
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nc/runnable-dynamic-schemas-from-config
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| 2023-10-19T18:34:36
|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,952,831,329
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PR_kwDOIPDwls5dTgP8
| 12,037
|
Revert "nc/runnable-dynamic-schemas-from-config"
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| 2023-10-19T18:25:11
| 2023-10-19T18:27:12
| 2023-10-19T18:27:02
|
COLLABORATOR
| null |
This reverts commit a46eef64a78edcbfb15d13165224b4574b668c18.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50ZXZV
| 12,035
|
Bedrock chain not working with AsyncCallbackHandler
|
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[] | null | 9
| 2023-10-19T18:09:39
| 2023-11-29T01:33:11
| null |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.317
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [X] Async
### Reproduction
Hello @agola11
I got this runtime warning:
RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
I try to stream over a websocket the generated tokens. When I try to add a AsyncCallbackHandler to manage this streaming and run acall the warning occurs and nothing is streamed out.
class StreamingLLMCallbackHandler(AsyncCallbackHandler):
def __init__(self, websocket):
self.websocket = websocket
self.intermediate_result = []
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
self.intermediate_result.append(token)
await self.websocket.send_text(token)
async def on_llm_end(self, token: str, **kwargs: Any) -> None:
await self.websocket.send_text("[END]")
stream_handler = StreamingLLMCallbackHandler()
model_kwargs = {
"max_tokens_to_sample": 8000,
"temperature": 0.7,
# "top_k": 250,
# "top_p": 1,
"stop_sequences" : ['STOP_LLM']
}
llm = Bedrock(
client=bedrock,
model_id="anthropic.claude-v2",
# provider_stop_sequence_key_name_map={'anthropic': 'stop_sequences'},
streaming=True,
callbacks=[stream_handler],
model_kwargs=model_kwargs
)
prompt_template = f"""
Human:
{{context}} {{question}}
Assistant:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain_type_kwargs = {"prompt": PROMPT}
pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_API_ENV)
chat_vectorstore = Pinecone.from_existing_index(index_name='intelligencechat1', embedding= OpenAIEmbeddings(openai_api_key= OPENAI_API_KEY), namespace='146')
chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs,
retriever=chat_vectorstore.as_retriever(search_kwargs={'k' : 4}),)
query = 'summarize'
result = await chain.acall(({"query": query}))
### Expected behavior
The expected behavior is that each token is streamed sequentially over the websocket.
the chain work in syncronous mode without 'acall'
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