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PR_kwDOIPDwls5cXM9k
| 11,596 |
add new field
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
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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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| 1,934,953,718 |
I_kwDOIPDwls5zVQz2
| 11,595 |
Multithreading support for TextSplitter
|
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[] | null | 1 | 2023-10-10T10:02:42 | 2023-10-10T15:30:40 | null |
NONE
| null |
### Feature request
Add optional multithreading support for `TextSplitter`, e.g for the loop in `TextSplitter.create_documents`:
https://github.com/langchain-ai/langchain/blob/e2a9072b806b1a45b0e4c107b30dddd0f67a453f/libs/langchain/langchain/text_splitter.py#L138-L153
Question: Is there anything opposing this idea / preventing it from a technical perspective?
### Motivation
Text splitting can take up significant time and resources if a custom length function is used to measure chunk length (e.g. based on a huggingface tokenizer's encode method), especially for the `RecursiveCharacterTextSplitter`.
Therefore we want to introduce multithreading support on a document level.
### Your contribution
Feature Request: https://github.com/langchain-ai/langchain/issues/11595
PR: https://github.com/langchain-ai/langchain/pull/11598
|
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PR_kwDOIPDwls5cW4Kr
| 11,594 |
fix: Change the docs of JSONAgentOutputParser
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[] | null | 2 | 2023-10-10T09:18:47 | 2023-10-11T21:05:54 | 2023-10-11T21:05:53 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/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.
-->
I am merely making some minor adjustments to the function documentation. I hope to provide a small assistance to LangChain.
- **Description:** Change the docs of JSONAgentOutputParser. It will be `JSON` better,
- **Issue:** no,
- **Dependencies:** no,
- **Tag maintainer:** @hwchase17,
- **Twitter handle:** Not worth mentioning.
|
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| 1,934,656,142 |
I_kwDOIPDwls5zUIKO
| 11,593 |
AzureOpenAI doesn't expose parameter to set custom HTTP headers.
|
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[] | null | 2 | 2023-10-10T07:56:04 | 2023-10-13T09:26:48 | null |
CONTRIBUTOR
| null |
### Feature request
Hey hi hello :)
Some organizations expose Azure OpenAI endpoints via some proxies that need additional HTTP headers. Currently AzureOpenAI class doesn't expose (at least I wasn't able to find one) a capability to set this. Similarly to what is possible in OpenAIEmbeddings ([https://api.python.langchain.com/en/latest/embeddings/langchain.embeddings.openai.OpenAIEmbeddings.html#langchain.embeddings.openai.OpenAIEmbeddings.headers](https://api.python.langchain.com/en/latest/embeddings/langchain.embeddings.openai.OpenAIEmbeddings.html#langchain.embeddings.openai.OpenAIEmbeddings.headers)). Something similar was already discussed ([https://github.com/langchain-ai/langchain/issues/2120](https://github.com/langchain-ai/langchain/issues/2120)) and embeddings have that already solved. It would be great to have that also for AzureOpenAI class.
### Motivation
I need this to get via proxy that needs additional HTTP headers.
### Your contribution
I don't think I can contribute to this.
|
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I_kwDOIPDwls5zT0OZ
| 11,592 |
Return ids for the document returned from the Similarity Search.
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| false | null |
[] | null | 2 | 2023-10-10T07:14:45 | 2023-10-14T16:57:07 | null |
NONE
| null |
### Feature request
I want to get the ids of the document returned when performing `similarity_search()` or `similarity_search_with_score()`. The id should be present in the metadata = {"id": id}
### Motivation
Want to update the metadata of the documents that are returned in the similarity search. This update can only be done if the the documents returned has the id in its metadata. When adding the documents to the vectordb, I am not adding the ids, as they are automatically generated if not passed.
### Your contribution
No contributions but below is the changes that can be made:
```
def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
return [
# TODO: Chroma can do batch querying,
# we shouldn't hard code to the 1st result
(Document(page_content=result[0], metadata=(result[1] | {'id': result[3]}) or {}), result[2])
for result in zip(
results["documents"][0],
results["metadatas"][0],
results["distances"][0],
results["ids"][0]
)
]
```
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PR_kwDOIPDwls5cVjiC
| 11,591 |
Add openvino backend support
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| false | null |
[] | null | 4 | 2023-10-10T06:26:37 | 2023-11-13T02:00:37 | null |
NONE
| null |
- **Description:** add openvino backend support by HuggingFace Optimum Intel,
- **Dependencies:** “optimum[openvino]”,
|
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I_kwDOIPDwls5zTRcS
| 11,590 |
Getting error "Just a moment...Enable JavaScript and cookies to continue" when loading website using WebBaseLoader
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| false | null |
[] | null | 2 | 2023-10-10T05:53:55 | 2023-11-20T07:43:52 | null |
NONE
| null |
### System Info
langchain==0.0.311
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://academyselfdefense.com/")
data = loader.load()
raw_text = data[0].page_content
print(raw_text)
```
### Expected behavior
load proper content in website instead of` Just a moment...Enable JavaScript and cookies to continue`
|
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| 1,934,334,394 |
I_kwDOIPDwls5zS5m6
| 11,589 |
Request for BedrockChat async functions(BedrockChat.agenerate).
|
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[] | null | 1 | 2023-10-10T04:36:10 | 2023-10-10T04:43:32 | null |
NONE
| null |
### Feature request
Is there any plan for supporting BedrockChat async calls?
### Motivation
I am using this api in a fastapi backend, while receiving data from bedrock I have to send data back to frontend. But for now the network is totally blocked, and streaming is no possible in this case.
### Your contribution
no
|
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| 1,934,299,075 |
PR_kwDOIPDwls5cU5QJ
| 11,588 |
Update modal.py
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[] | null | 3 | 2023-10-10T04:13:10 | 2023-10-19T06:40:38 | 2023-10-19T06:40:38 |
CONTRIBUTOR
| null |
feat: Raise KeyError when 'prompt' key is missing in JSON response
This commit updates the error handling in the code to raise a KeyError when the 'prompt' key is not found in the JSON response. This change makes the code more explicit about the nature of the error, helping to improve clarity and debugging.
@baskaryan, @eyurtsev.
|
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| 1,934,209,959 |
I_kwDOIPDwls5zSbOn
| 11,587 |
similarity_search_with_relevance_scores is not working properly with Pinecone
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[] | null | 3 | 2023-10-10T02:51:40 | 2023-10-17T03:19:33 | 2023-10-17T03:19:33 |
NONE
| null |
### System Info
Langchain Version: 0.0.311
Python: 3.10.9
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I loaded the my vector store from **pinecone** and try to find top-3 most similar documents with the relevance score
```
vectorstore = Pinecone(index, embed.embed_query, text_field)
answers = vectorstore.similarity_search_with_score(query, 3)
for item in answers:
print(item[1]) # print out score
```
By running above code, I can get relevance score for those 3 documents:
```
0.851780415
0.851505935
0.850369573
```
It looks as expected
But when I try to use `similarity_search_with_relevance_scores` to find out similar docs:
```
answers= vectorstore.similarity_search_with_relevance_scores(query, score_threshold=0.8)
```
From my understanding it should return me at least 3 docs since we do have docs similar higher than 0.85, but I got warning `No relevant docs were retrieved using the relevance score threshold 0.8` with an empty return
And I tried vectorstore retriever as well and got same warning and empty return:
```
retriever = vectorstore.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"score_threshold": 0.8}
)
answer = retriever.get_relevant_documents(query)
print(answer)
```
Did I use the score function incorrectly ? If not, is there any other ways to query with score_threshold?
Thanks
### Expected behavior
When using
```
answers= vectorstore.similarity_search_with_relevance_scores(query, score_threshold=0.8)
```
or
```
retriever = vectorstore.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"score_threshold": 0.8}
)
answer = retriever.get_relevant_documents(query)
print(answer)
```
It should return me few of doc that have >= 0.8 similarity.
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| 1,934,170,364 |
PR_kwDOIPDwls5cUcmB
| 11,586 |
Update README.md
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[] | null | 1 | 2023-10-10T02:20:44 | 2023-10-10T02:21:56 | 2023-10-10T02:21:56 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/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_kwDOIPDwls5cUamS
| 11,585 |
Add dedicated `type` attribute to be used solely for serialization purposes
|
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[] | null | 1 | 2023-10-10T02:13:02 | 2023-10-11T19:06:43 | 2023-10-11T19:06:42 |
COLLABORATOR
| null |
Adds standard `type` field for all messages that will be serialized/validated by pydantic.
* The presence of `type` makes it easier for developers consuming schemas to write client code to serialize/deserialize.
* In LangServe `type` will be used for both validation and will appear in the generated openapi specs
|
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| 1,934,118,870 |
I_kwDOIPDwls5zSE_W
| 11,584 |
Discord Integration Toolkit
|
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[] | null | 1 | 2023-10-10T01:39:47 | 2023-12-01T05:29:28 | 2023-11-28T07:13:41 |
NONE
| null |
### Feature request
Introduce a comprehensive Discord Integration Toolkit to Langchain. This would allow a more seamless and direct interaction with the Discord API through an Agent interface. It should encompass capabilities like message dispatch, channel navigation, user role management, and channel administration.
### Motivation
At present, Langchain offers a limited set of functionalities for interfacing with the Discord API. Specifically, the available method is the DiscordChatLoader, necessitating manual data downloading and uploading in a CSV format. This approach not only lacks versatility but is cumbersome. Furthermore, there's an absence of functions that would empower an LLM Agent to undertake tasks like messaging, channel searches, role assignments, and channel handling on Discord.
### Your contribution
We are initiating the development phase for this proposal and intend to submit a PR once the feature reaches completion.
|
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PR_kwDOIPDwls5cUEH7
| 11,583 |
Fix CohereRerank configuration
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[] | null | 2 | 2023-10-10T00:34:13 | 2023-10-10T11:08:55 | 2023-10-10T06:26:35 |
CONTRIBUTOR
| null |
**Description:** CohereRerank is missing `cohere_api_key` as a field and since extras are forbidden, it is not possible to pass-in the key. The only way is to use an env variable named `COHERE_API_KEY`.
For example, if trying to create a compressor like this:
```python
cohere_api_key = "......Cohere api key......"
compressor = CohereRerank(cohere_api_key=cohere_api_key)
```
you will get the following error:
```
File "/langchain/.venv/lib/python3.10/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for CohereRerank
cohere_api_key
extra fields not permitted (type=value_error.extra)
```
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PR_kwDOIPDwls5cT8hk
| 11,582 |
Semi-structured and Multi-modal RAG cookbooks
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5cTsRa
| 11,581 |
Update docs to specify Indexing-API-compatible vectorstores
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CONTRIBUTOR
| null |
**Description:** Update Indexing API docs to specify vectorstores that are compatible with the Indexing API. I add a unit test to remind developers to update the documentation whenever they add or change a vectorstore in a way that affects compatibility. For the unit test I repurposed existing code from [here](https://github.com/langchain-ai/langchain/blob/v0.0.311/libs/langchain/langchain/indexes/_api.py#L245-L257).
This is my first PR to an open source project. This is a trivially simple PR whose main purpose is to make me more comfortable submitting Langchain PRs. If this PR goes through I plan to submit PRs with more substantive changes in the near future.
**Issue:** Resolves [10482](https://github.com/langchain-ai/langchain/discussions/10482).
**Dependencies:** No new dependencies.
**Twitter handle:** None.
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| 1,933,898,140 |
I_kwDOIPDwls5zRPGc
| 11,580 |
AmazonAPIGatewayEmbeddings class for text embeddings
|
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[] | null | 0 | 2023-10-09T22:10:30 | 2023-10-09T22:12:35 | null |
NONE
| null |
### Feature request
As of today, it's not possible to use Amazon API Gateway for exposing embeddings model and use it as part of a chain (e.g. ConversationalRetrievalChain). As of today, AmazonAPIGateway can be used only as LLM for text generation, but you cannot use it as embeddings for text embeddings generation (e.g. as part of ConversationalRetrievalChain)
### Motivation
Amazon API Gateway can be adopted for both Text generation and Text Embeddings. Amazon Bedrock provides different type of models (LLMs and Embeddings models). In this way, developers can use Amazon API Gateway for Retrieval Augmented Generation solutions
### Your contribution
The class can be defined as following:
```
from langchain.embeddings.bedrock import Embeddings
import requests
from typing import List
class AmazonAPIGatewayEmbeddings(Embeddings):
def __init__(self, api_url, headers):
self.api_url = api_url
self.headers = headers
def embed_documents(self, texts: List[str]) -> List[List[float]]:
results = []
for text in texts:
response = requests.post(
self.api_url,
json={"inputs": text},
headers=self.headers
)
results.append(response.json()[0]["embedding"])
return results
def embed_query(self, text: str) -> List[float]:
response = requests.post(
self.api_url,
json={"inputs": text},
headers=self.headers
)
return response.json()[0]["embedding"]
---
embeddings = AmazonAPIGatewayEmbeddings(
api_url=f"{api_url}/invoke_model?model_id={model_id}",
headers={
... # Required headers for the API invocation
}
)
embeddings.embed_query("Hello, how are you?")
```
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| 11,579 |
Arcee.ai LLM & Retriever integration
|
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[] | null | 2 | 2023-10-09T21:42:59 | 2023-10-10T17:20:46 | 2023-10-10T17:20:45 |
CONTRIBUTOR
| null |
- **Description:** This PR introduces a new LLM and Retriever API to https://arcee.ai for the python client
- **Issue:** implements the integrations as requested in #11578 ,
- **Dependencies:** no dependencies are required,
- **Tag maintainer:** @hwchase17
- **Twitter handle:** shwooobham
**✅ `make format`, `make lint` and `make test` runs locally.**
```shell
=========== 1245 passed, 277 skipped, 20 warnings in 16.26s ===========
./scripts/check_pydantic.sh .
./scripts/check_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run black . --check
All done! ✨ 🍰 ✨
1818 files would be left unchanged.
[ "." = "" ] || poetry run mypy .
Success: no issues found in 1815 source files
[ "." = "" ] || poetry run black .
All done! ✨ 🍰 ✨
1818 files left unchanged.
[ "." = "" ] || poetry run ruff --select I --fix .
poetry run codespell --toml pyproject.toml
poetry run codespell --toml pyproject.toml -w
```
**Contributions**
1. Arcee (langchain/llms), ArceeRetriever (langchain/retrievers), ArceeWrapper (langchain/utilities)
2. docs for Arcee (llms/arcee.py) and ArceeRetriever(retrievers/arcee.py)
3.
cc: @jacobsolawetz @ben-epstein
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I_kwDOIPDwls5zRCi8
| 11,578 |
Support of Arcee.ai LLM and Retrievers
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[] | null | 2 | 2023-10-09T21:26:51 | 2023-10-10T19:43:10 | 2023-10-10T19:43:10 |
CONTRIBUTOR
| null |
### Feature request
I propose to add the Python client for Arcee.ai as an LLM and retriever.
`arcee.py` under [langchain/utilities](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/utilities)
`arcee.py` under [langchain/llms](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/llms)
`arcee.py` under [langchain/retrievers](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/retrievers)
```python
class Arcee(LLM):
# client for Arcee's Domain Adapted Language Models (DALMs)
#
```
```python
class ArceeRetriever(BaseRetriever):
# retriever for Arcee's DALMs
#
```
See: Client docs
https://github.com/arcee-ai/arcee-python
### Motivation
Arcee.ai offers seamless Domain Adaptation with its Specialized Domain Adapted Language Model system.
I want to utilize these adapted language models on https://arcee.ai and build applications with LangChain.
### Your contribution
Discussions - https://github.com/arcee-ai/arcee-python/issues/15
PR - https://github.com/langchain-ai/langchain/pull/11579
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Sl integration
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[] | null | 1 | 2023-10-09T20:54:15 | 2023-10-09T20:54:27 | 2023-10-09T20:54:27 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/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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| 11,576 |
Add option to specify metadata columns in CSV loader
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[] | null | 2 | 2023-10-09T20:49:30 | 2023-10-09T21:56:46 | 2023-10-09T21:56:45 |
CONTRIBUTOR
| null |
#### Description
This PR adds the option to specify additional metadata columns in the CSVLoader beyond just `Source`.
The current CSV loader includes all columns in `page_content` and if we want to have columns specified for `page_content` and `metadata` we have to do something like the below.:
```
csv = pd.read_csv(
"path_to_csv"
).to_dict("records")
documents = [
Document(
page_content=doc["content"],
metadata={
"last_modified_by": doc["last_modified_by"],
"point_of_contact": doc["point_of_contact"],
}
) for doc in csv
]
```
#### Usage
Example Usage:
```
csv_test = CSVLoader(
file_path="path_to_csv",
metadata_columns=["last_modified_by", "point_of_contact"]
)
```
Example CSV:
```
content, last_modified_by, point_of_contact
"hello world", "Person A", "Person B"
```
Example Result:
```
Document {
page_content: "hello world"
metadata: {
row: '0',
source: 'path_to_csv',
last_modified_by: 'Person A',
point_of_contact: 'Person B',
}
```
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[] | null | 1 | 2023-10-09T19:25:32 | 2023-10-19T15:17:46 | 2023-10-19T15:06:56 |
CONTRIBUTOR
| null |
- **Description:** Adding a notebook for Press Release data from Kay.ai, as discussed offline
- **Tag maintainer:** @baskaryan @hwchase17
- **Twitter handle:** https://twitter.com/kaydotai https://twitter.com/vishalrohra_
|
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| 1,933,650,569 |
I_kwDOIPDwls5zQSqJ
| 11,574 |
DOC: Wolfram Agent Link broken on README
|
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open
| false | null |
[] | null | 0 | 2023-10-09T19:15:40 | 2023-10-09T19:17:47 | null |
NONE
| null |
### Issue with current documentation:
End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
This link leads to a 404. A quick google search does not find a working page.
### Idea or request for content:
_No response_
|
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| 1,933,642,303 |
PR_kwDOIPDwls5cSmNH
| 11,573 |
LangChain cli fix a few bugs
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[] | null | 1 | 2023-10-09T19:09:09 | 2023-10-09T20:30:18 | 2023-10-09T20:30:17 |
COLLABORATOR
| null |
Code was assuming that `git` and `poetry` exist. In addition, it was not ignoring pycache files that get generated during run time
|
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I_kwDOIPDwls5zQIOd
| 11,571 |
Support for case insensitive query search something like "$regex" instead of "$contains"
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[] | null | 15 | 2023-10-09T18:43:18 | 2023-10-10T20:31:04 | 2023-10-10T20:31:04 |
NONE
| null |
### Issue you'd like to raise.
To get documents from collections (vector db) there is a method:
```python
class Collection(BaseModel):
name: str
id: UUID
metadata: Optional[CollectionMetadata] = None
_client: "API" = PrivateAttr()
_embedding_function: Optional[EmbeddingFunction] = PrivateAttr()
def __init__(
self,
client: "API",
name: str,
id: UUID,
embedding_function: Optional[EmbeddingFunction] = ef.DefaultEmbeddingFunction(),
metadata: Optional[CollectionMetadata] = None,
):
self._client = client
self._embedding_function = embedding_function
super().__init__(name=name, metadata=metadata, id=id)
'''
'''
def get(
self,
ids: Optional[OneOrMany[ID]] = None,
where: Optional[Where] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
where_document: Optional[WhereDocument] = None,
include: Include = ["metadatas", "documents"],
) -> GetResult:
"""Get embeddings and their associate data from the data store. If no ids or where filter is provided returns
all embeddings up to limit starting at offset.
Args:
ids: The ids of the embeddings to get. Optional.
where: A Where type dict used to filter results by. E.g. `{"$and": ["color" : "red", "price": {"$gte": 4.20}]}`. Optional.
limit: The number of documents to return. Optional.
offset: The offset to start returning results from. Useful for paging results with limit. Optional.
where_document: A WhereDocument type dict used to filter by the documents. E.g. `{$contains: {"text": "hello"}}`. Optional.
include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional.
Returns:
GetResult: A GetResult object containing the results.
"""
where = validate_where(where) if where else None
where_document = (
validate_where_document(where_document) if where_document else None
)
ids = validate_ids(maybe_cast_one_to_many(ids)) if ids else None
include = validate_include(include, allow_distances=False)
return self._client._get(
self.id,
ids,
where,
None,
limit,
offset,
where_document=where_document,
include=include,
)
```
To query documents by searching for a particular term in the document `where_document = {"$contains": "langchain"}` can be passed to the `get()` method. This value for the key/operator `$contains` is case sensitive. How to search for keywords irrespective of case?
### Suggestion:
I want to extract entities from a sentence and pass it get documents from the chromadb that contains that words. But this entities need to be case sensitive. If the LLM outputs a different case like for example, the document contains the keyword: "Langchain", but asking llm to extract the entity from the sentence it isnt always sure that it will generate "Langchain", it can output: "langchain". This can be handled if the first letter of the entities generated is capitalized. But this may not work for keywords like OpenCV.
|
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| 1,933,597,293 |
PR_kwDOIPDwls5cScRl
| 11,570 |
Add Azure Redis example
|
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[] | null | 1 | 2023-10-09T18:34:36 | 2023-10-09T22:49:09 | 2023-10-09T20:27:03 |
CONTRIBUTOR
| null |
**Description**
This PR adds an additional Example to the Redis integration documentation. [The example](https://learn.microsoft.com/azure/azure-cache-for-redis/cache-tutorial-vector-similarity) is a step-by-step walkthrough of using Azure Cache for Redis and Azure OpenAI for vector similarity search, using LangChain extensively throughout.
**Issue**
Nothing specific, just adding an additional example.
**Dependencies**
None.
**Tag Maintainer**
Tagging @hwchase17 :)
|
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| 1,933,585,496 |
PR_kwDOIPDwls5cSZnT
| 11,569 |
Modify Anyscale integration to work with Anyscale Endpoint
|
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| false | null |
[] | null | 2 | 2023-10-09T18:27:43 | 2023-10-12T20:24:20 | 2023-10-12T15:41:26 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/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.
-->
**Description:** Modify Anyscale integration to work with [Anyscale Endpoint](https://docs.endpoints.anyscale.com/)
and it supports invoke, async invoke, stream and async invoke features
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PR_kwDOIPDwls5cSVSU
| 11,568 |
Add repl library
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[] | null | 1 | 2023-10-09T18:13:44 | 2023-10-11T21:31:56 | null |
COLLABORATOR
| null |
- [ ] deprecation notice in main lib (how do we do those?)
- [ ] add CI jobs
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
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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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|
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PR_kwDOIPDwls5cSUat
| 11,567 |
feat: add momento vector index as a vector store provider
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[] | null | 4 | 2023-10-09T18:11:58 | 2023-10-12T17:06:19 | 2023-10-09T21:03:00 |
CONTRIBUTOR
| null |
**Description**:
- Added Momento Vector Index (MVI) as a vector store provider. This includes an implementation with docstrings, integration tests, a notebook, and documentation on the docs pages.
- Updated the Momento dependency in pyproject.toml and the lock file to enable access to MVI.
- Refactored the Momento cache and chat history session store to prefer using "MOMENTO_API_KEY" over "MOMENTO_AUTH_TOKEN" for consistency with MVI. This change is backwards compatible with the previous "auth_token" variable usage. Updated the code and tests accordingly.
**Dependencies**:
- Updated Momento dependency in pyproject.toml.
**Testing**:
- Run the integration tests with a Momento API key. Get one at the [Momento Console](https://console.gomomento.com) for free. MVI is available in AWS us-west-2 with a superuser key.
- `MOMENTO_API_KEY=<your key> poetry run pytest tests/integration_tests/vectorstores/test_momento_vector_index.py`
**Tag maintainer:**
@eyurtsev
**Twitter handle**:
Please mention @momentohq for this addition to langchain. With the integration of Momento Vector Index, Momento caching, and session store, Momento provides serverless support for the core langchain data needs.
Also mention @mlonml for the integration.
|
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PR_kwDOIPDwls5cSSB2
| 11,566 |
Accelerating Math Utils with SimSIMD
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[] | null | 1 | 2023-10-09T18:03:56 | 2023-10-09T22:30:39 | 2023-10-09T21:56:55 |
CONTRIBUTOR
| null |
LangChain relies on NumPy to compute cosine distances, which becomes a bottleneck with the growing dimensionality and number of embeddings. To avoid this bottleneck, in our libraries at [Unum](https://github.com/unum-cloud), we have created a specialized package - [SimSIMD](https://github.com/ashvardanian/simsimd), that knows how to use newer hardware capabilities. Compared to SciPy and NumPy, it reaches 3x-200x performance for various data types. Since publication, several LangChain users have asked me if I can integrate it into LangChain to accelerate their workflows, so here I am 🤗
## Benchmarking
To conduct benchmarks locally, run this in your Jupyter:
```py
import numpy as np
import scipy as sp
import simsimd as simd
import timeit as tt
def cosine_similarity_np(X: np.ndarray, Y: np.ndarray) -> np.ndarray:
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
def cosine_similarity_sp(X: np.ndarray, Y: np.ndarray) -> np.ndarray:
return 1 - sp.spatial.distance.cdist(X, Y, metric='cosine')
def cosine_similarity_simd(X: np.ndarray, Y: np.ndarray) -> np.ndarray:
return 1 - simd.cdist(X, Y, metric='cosine')
X = np.random.randn(1, 1536).astype(np.float32)
Y = np.random.randn(1, 1536).astype(np.float32)
repeat = 1000
print("NumPy: {:,.0f} ops/s, SciPy: {:,.0f} ops/s, SimSIMD: {:,.0f} ops/s".format(
repeat / tt.timeit(lambda: cosine_similarity_np(X, Y), number=repeat),
repeat / tt.timeit(lambda: cosine_similarity_sp(X, Y), number=repeat),
repeat / tt.timeit(lambda: cosine_similarity_simd(X, Y), number=repeat),
))
```
## Results
I ran this on an M2 Pro Macbook for various data types and different number of rows in `X` and reformatted the results as a table for readability:
| Data Type | NumPy | SciPy | SimSIMD |
| :--- | ---: | ---: | ---: |
| `f32, 1` | 59,114 ops/s | 80,330 ops/s | 475,351 ops/s |
| `f16, 1` | 32,880 ops/s | 82,420 ops/s | 650,177 ops/s |
| `i8, 1` | 47,916 ops/s | 115,084 ops/s | 866,958 ops/s |
| `f32, 10` | 40,135 ops/s | 24,305 ops/s | 185,373 ops/s |
| `f16, 10` | 7,041 ops/s | 17,596 ops/s | 192,058 ops/s |
| `f16, 10` | 21,989 ops/s | 25,064 ops/s | 619,131 ops/s |
| `f32, 100` | 3,536 ops/s | 3,094 ops/s | 24,206 ops/s |
| `f16, 100` | 900 ops/s | 2,014 ops/s | 23,364 ops/s |
| `i8, 100` | 5,510 ops/s | 3,214 ops/s | 143,922 ops/s |
It's important to note that SimSIMD will underperform if both matrices are huge.
That, however, seems to be an uncommon usage pattern for LangChain users.
You can find a much more detailed performance report for different hardware models here:
- [Apple M2 Pro](https://ashvardanian.com/posts/simsimd-faster-scipy/#appendix-1-performance-on-apple-m2-pro).
- [4th Gen Intel Xeon Platinum](https://ashvardanian.com/posts/simsimd-faster-scipy/#appendix-2-performance-on-4th-gen-intel-xeon-platinum-8480).
- [AWS Graviton 3](https://ashvardanian.com/posts/simsimd-faster-scipy/#appendix-3-performance-on-aws-graviton-3).
## Additional Notes
1. Previous version used `X = np.array(X)`, to repackage lists of lists. It's an anti-pattern, as it will use double-precision floating-point numbers, which are slow on both CPUs and GPUs. I have replaced it with `X = np.array(X, dtype=np.float32)`, but a more selective approach should be discussed.
2. In numerical computations, it's recommended to explicitly define tolerance levels, which were previously avoided in `np.allclose(expected, actual)` calls. For now, I've set absolute tolerance to distance computation errors as 0.01: `np.allclose(expected, actual, atol=1e-2)`.
---
- **Dependencies:** adds `simsimd` dependency
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @ashvardanian
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I_kwDOIPDwls5zP4Xc
| 11,565 |
DOC: Remove the Link Icon in the footer due to the docusaurus default settings
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[] | null | 1 | 2023-10-09T17:57:51 | 2023-10-09T17:59:58 | null |
NONE
| null |
### Issue with current documentation:
The current Docusaurus default settings display a link icon in the footer of the website. This icon is a small hyperlink symbol that appears next to external links in the footer. While this may be a helpful feature for some websites, it may not align with the design or functional requirements of our documentation site.
### Idea or request for content:
The goal of this issue is to remove the link icon from the footer of our Docusaurus-powered website. This will result in a cleaner and more minimalistic footer design.
Expected Behavior:
The link icon should be removed from the footer, ensuring that external links are presented without the additional icon.
Attachments:
Current Footer:
|
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PR_kwDOIPDwls5cSNve
| 11,564 |
Add callback function to RunnablePassthrough
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[] | null | 1 | 2023-10-09T17:49:36 | 2023-10-12T14:10:17 | 2023-10-12T14:10:16 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/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_kwDOIPDwls5cSH9q
| 11,563 |
[LLMonitor Callback Handler]: Add error handling
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[] | null | 2 | 2023-10-09T17:28:49 | 2023-10-09T20:26:36 | 2023-10-09T20:26:36 |
CONTRIBUTOR
| null |
Wraps every callback handler method in error handlers to avoid breaking users' programs when an error occurs inside the handler.
Thanks @valdo99 for the suggestion 🙂
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redirect langsmith guides
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PR_kwDOIPDwls5cR8Yi
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Use correct tokenizer for Bedrock/Anthropic LLMs
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[] | null | 5 | 2023-10-09T16:47:30 | 2023-12-04T15:44:56 | 2023-10-12T15:41:52 |
CONTRIBUTOR
| null |
**Description**
This PR implements the usage of the correct tokenizer in Bedrock LLMs, if using anthropic models.
**Issue:** #11560
**Dependencies:** optional dependency on `anthropic` python library.
**Twitter handle:** jtolgyesi
<!-- 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_kwDOIPDwls5zPe3q
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Incorrect token counting in Bedrock LLMs
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[] | null | 1 | 2023-10-09T16:37:33 | 2023-10-09T16:45:35 | null |
CONTRIBUTOR
| null |
### System Info
LangChain v0.0.304
### Who can help?
@hwchase17 @ag
### 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
Use a combine documents chain with Bedrock/anthropic.claude-v2 LLM:
```python
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import Bedrock
llm = Bedrock(model_id="anthropic.claude-v2")
chain = load_qa_chain(llm, chain_type="map_reduce", verbose=verbose, **kwargs)
chain.run(...)
```
### Expected behavior
The chain runs correctly.
Instead, a series of warnings are raised about missing `transformers` library, about too long context passed to tokenizer, etc. etc., and at last the chain fails to run.
## Background
The default implementation of `get_token_ids` and `get_num_tokens` in `BaseLanguageModel` uses a GPT-2 based tokenizer. The `Bedrock` LLM implementation does not override these methods, so it tries to tokenize the text with the incorrect tokenizer. In the particular case when using `anthropic.claude-v2` model, this causes incorrect token counting, requires an otherwise unnecessary dynamic dependencies (the transformers library), and emits a series of warnings about too long input text passed for tokenizer.
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PR_kwDOIPDwls5cRp9D
| 11,559 |
Reset deanonymizer mapping
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[] | null | 1 | 2023-10-09T15:49:56 | 2023-10-09T18:11:05 | 2023-10-09T18:11:05 |
CONTRIBUTOR
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@hwchase17 @baskaryan
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PR_kwDOIPDwls5cRmZJ
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Support all presidio entities
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[] | null | 1 | 2023-10-09T15:39:58 | 2023-10-09T18:10:46 | 2023-10-09T18:10:46 |
CONTRIBUTOR
| null |
https://microsoft.github.io/presidio/supported_entities/
@baskaryan @hwchase17
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PR_kwDOIPDwls5cRlvI
| 11,557 |
Better deanonymizer matching strategy
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[] | null | 1 | 2023-10-09T15:38:06 | 2023-10-09T18:10:29 | 2023-10-09T18:10:29 |
CONTRIBUTOR
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@baskaryan, @hwchase17
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PR_kwDOIPDwls5cRgFP
| 11,556 |
Update Dockerfile.base
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[] | null | 1 | 2023-10-09T15:22:27 | 2023-10-09T15:43:05 | 2023-10-09T15:43:04 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5cRb-X
| 11,555 |
bump 311
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COLLABORATOR
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I_kwDOIPDwls5zO5Em
| 11,554 |
On the class description page there is no list with links to class methodsDOC: <Please write a comprehensive title after the 'DOC: ' prefix>
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[] | null | 0 | 2023-10-09T15:02:05 | 2023-10-09T15:04:13 | null |
NONE
| null |
### Issue with current documentation:
On the class description page there is no list with links to class methods
The class:
langchain.chains.retrieval_qa.base.RetrievalQA
https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html#langchain.chains.retrieval_qa.base.RetrievalQA
List as on this page:
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler.html#langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5cRWDM
| 11,553 |
feature: add metadata information into the embedding file before uplo…
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[] | null | 3 | 2023-10-09T14:59:21 | 2023-10-11T23:05:14 | 2023-10-11T23:05:13 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** In this modified version of the function, if the metadatas parameter is not None, the function includes the corresponding metadata in the JSON object for each text. This allows the metadata to be stored alongside the text's embedding in the vector store.
-
- **Issue:** #10924
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
@agola11
- **Twitter handle:** @MelliJoaco
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| 1,933,241,258 |
PR_kwDOIPDwls5cROIs
| 11,552 |
RunnablePassthrough: In code documentation
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[] | null | 1 | 2023-10-09T14:40:07 | 2023-10-09T20:02:17 | 2023-10-09T20:02:16 |
COLLABORATOR
| null |
Add in code documentation for a runnable passthrough
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| 1,933,220,532 |
PR_kwDOIPDwls5cRJnv
| 11,551 |
RFC: Implement stream/transform in RunnableRetry
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[] | null | 2 | 2023-10-09T14:29:44 | 2023-10-10T14:52:49 | null |
COLLABORATOR
| null |
Yield a "stream reset" marker before starting the next attempt
Open question: How to deal with this best in RunnableSequence/RunnableMap? The next steps in the sequence are probably not designed to handle the reset marker, should RunnableSequence retry all steps after "current" once it sees a retry marker?
<!-- Thank you for contributing to LangChain!
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- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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_kwDOIPDwls5cQ5i_
| 11,550 |
Module documentation for langchain runnables
|
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[] | null | 1 | 2023-10-09T13:59:29 | 2023-10-09T20:02:31 | 2023-10-09T20:02:30 |
COLLABORATOR
| null |
Add in code documentation for langchain runnables module.
|
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PR_kwDOIPDwls5cQdsa
| 11,549 |
doc: Fix documentation about n-gram overlap
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[] | null | 3 | 2023-10-09T13:04:02 | 2023-10-11T23:27:00 | 2023-10-11T23:26:56 |
CONTRIBUTOR
| null |
This commit fixes the documentation about n-gram overlap that unrelated variables are declared.
Replace this entire comment with:
- **Description:** Fix the documentation in https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap. It's currently declaring unrelated variables, for example, `examples` local variable is declared twice and the first one is overwritten immediately.
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** @dosuken123
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_kwDOIPDwls5zNoCM
| 11,548 |
Issue: <Getting error while setting up a RetrievalQA Conversation with Faiss_cpu prefix>
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[] | null | 7 | 2023-10-09T12:24:24 | 2023-10-09T18:48:22 | null |
NONE
| null |
### Issue you'd like to raise.
I am getting this error while setting a RetrievalQA using FAISS, the code is as follows:
Using the following version on a MAC : python 3.10, Faiss_cpu 1.7.4, langchain 0.0.310
embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME')
faiss_index = os.environ.get('FAISS_INDEX')
embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
self.db = FAISS.load_local(faiss_index, embeddings)
retriever = self.db.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 10}
)
prompt = [
("human", "Hello"),
("assistant", "Hi there!"),
]
qa = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="refine",
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={"prompt": prompt}
)
res = qa({
"query": query,
"prompt": prompt,
"context": "You are helpful"
})
The error is as follows:
([ErrorWrapper(exc=ExtraError(), loc=('prompt',))], <class 'langchain.chains.combine_documents.refine.RefineDocumentsChain'>)
The console show the following:
rompt : [('human', 'Hello'), ('assistant', 'Hi there!')]
self.llm : LlamaCpp
Params: {'model_path': 'models/llama-2-7b-chat.Q8_0.gguf', 'suffix': None, 'max_tokens': 256, 'temperature': 0.8, 'top_p': 0.9, 'logprobs': None, 'echo': False, 'stop_sequences': [], 'repeat_penalty': 1.1, 'top_k': 40}
Failed while retrieve documents: ([ErrorWrapper(exc=ExtraError(), loc=('prompt',))], <class 'langchain.chains.combine_documents.refine.RefineDocumentsChain'>)
Query response None
### Suggestion:
_No response_
|
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| 1,932,912,761 |
I_kwDOIPDwls5zNeh5
| 11,547 |
Support max_marginal_relevance_search for ElasticSearchStore
|
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[] | null | 2 | 2023-10-09T12:02:23 | 2023-10-10T23:00:10 | null |
NONE
| null |
### System Info
max_marginal_relevance_search is mentioned in the ElasticSearchStore python documentation, but when calling the referenced API with langchain 0.0.310 and Python 3.9 I receive a NotImplementedError.
https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html#langchain.vectorstores.elasticsearch.ElasticsearchStore.max_marginal_relevance_search
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
vector_store = ElasticsearchStore(
embedding=OpenAIEmbeddings(),
index_name="{INDEX_NAME}",
es_cloud_id="{CLOUD_ID}",
es_user="{USER}",
es_password="{PASSWORD}"
)
pages = vector_store.max_marginal_relevance_search("test query")
```
### Expected behavior
max_marginal_relevance_search functions as per the documentation.
|
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| 1,932,899,628 |
I_kwDOIPDwls5zNbUs
| 11,546 |
Issue: Add Examples of implementing Tree of Thoughts using Langchain
|
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[] | null | 0 | 2023-10-09T11:54:58 | 2023-10-09T11:57:07 | null |
NONE
| null |
### Issue you'd like to raise.
https://arxiv.org/abs/2305.10601
### Suggestion:
https://arxiv.org/abs/2305.10601
|
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I_kwDOIPDwls5zMz6Z
| 11,545 |
Issue: Inference Client in Lang chain
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open
| false | null |
[] | null | 4 | 2023-10-09T10:15:14 | 2023-11-06T14:30:18 | null |
NONE
| null |
### Issue you'd like to raise.
Hi, I am trying to use Hugging Face model for langchain. However, I got this error :
"FutureWarning: '__init__' (from 'huggingface_hub.inference_api') is deprecated and will be removed from version '0.19.0'. `InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out this guide to learn how to convert your script to use it: https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client."
This is my code :
`
os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingFace_api_key
llm_repo_id = "google/flan-t5-xxl"
def generate_pet_name(animal_type,pet_color):
llm = HuggingFaceHub(
repo_id=llm_repo_id, model_kwargs={"temperature": 0.5})
prompt_template = PromptTemplate(
input_variables = ['animal_type','animal_color'],
template = "I have an {animal_type} with {animal_color} in color. Suggest me five name which sound"
+ "appropriate with the color for my {animal_type}")
name_chain = LLMChain(llm=llm,prompt=prompt_template,output_key="pet_name")
response = name_chain({'animal_type':animal_type,'animal_color':pet_color})
return response
`
How do I get rid of this warning.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5cOykg
| 11,543 |
Llama doc: add 'language' to the response message
|
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[] | null | 2 | 2023-10-09T08:14:41 | 2023-10-12T00:06:04 | 2023-10-12T00:06:04 |
CONTRIBUTOR
| null |
<!--
- **Description:** add 'language' to the reponse message in the Llama doc,
- **Issue:** None,
- **Dependencies:** None,
- **Tag maintainer:** None,
- **Twitter handle:** None
-->
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| 1,932,397,687 |
I_kwDOIPDwls5zLgx3
| 11,541 |
User-Agent needs to be set for RecursiveUrlLoader
|
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| false | null |
[] | null | 1 | 2023-10-09T06:37:51 | 2023-10-09T06:40:09 | null |
NONE
| null |
### Feature request
Based on the discussion in : https://github.com/langchain-ai/langchain/issues/11540
The WebBaseLoader in LangChain has a default User-Agent set in the session headers, and this could be a good enhancement for the RecursiveUrlLoader as well.
Here's a potential solution:
class RecursiveUrlLoader(BaseLoader):
"""Load all child links from a URL page."""
def __init__(
self,
url: str,
max_depth: Optional[int] = 2,
use_async: Optional[bool] = None,
extractor: Optional[Callable[[str], str]] = None,
metadata_extractor: Optional[Callable[[str, str], str]] = None,
exclude_dirs: Optional[Sequence[str]] = (),
timeout: Optional[int] = 10,
prevent_outside: Optional[bool] = True,
link_regex: Union[str, re.Pattern, None] = None,
headers: Optional[dict] = None,
check_response_status: bool = False,
) -> None:
...
self.headers = headers if headers is not None else {"User-Agent": "Mozilla/5.0"}
...
### Motivation
The RecursiveUrlLoader need to have an implicit user-Agent defined with the session like in WebBaseLoader(https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/document_loaders/web_base.py) else some of the websites we are trying to scrape gives a forbidden error.
Troubleshooting the error took a lot of time and finally we realised that it was due to the lack of appropriate headers.
loader = RecursiveUrlLoader(url=web_page,max_depth=1,extractor=lambda x: Soup(x, "html.parser").text)
docs = loader.load()
docs[0]
Document(page_content='\n403 Forbidden\n\n403 Forbidden\nnginx\n\n\n', metadata={'source': '.....', 'title': '403 Forbidden', 'language': None})
from fake_useragent import UserAgent
header_template = {}
header_template["User-Agent"] = UserAgent().random
loader = RecursiveUrlLoader(url=web_page,max_depth=1,headers=header_template,extractor=lambda x: Soup(x, "html.parser").text)
docs = loader.load()
docs[0]
Document(page_content="Hello and Welcome to....)
### Your contribution
yes
|
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| 1,932,355,135 |
I_kwDOIPDwls5zLWY_
| 11,540 |
Recursive URL doesn't work on some websites until User-Agent is added
|
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open
| false | null |
[] | null | 1 | 2023-10-09T05:57:59 | 2023-10-09T06:04:23 | null |
NONE
| null |
### System Info
Python: 3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
>>> loader = RecursiveUrlLoader(url=web_page,max_depth=1,extractor=lambda x: Soup(x, "html.parser").text)
>>> docs = loader.load()
>>> docs[0]
Document(page_content='\n403 Forbidden\n\n403 Forbidden\nnginx\n\n\n', metadata={'source': '.....', 'title': '403 Forbidden', 'language': None})
>>> from fake_useragent import UserAgent
>>> header_template = {}
>>> header_template["User-Agent"] = UserAgent().random
>>> loader = RecursiveUrlLoader(url=web_page,max_depth=1,headers=header_template,extractor=lambda x: Soup(x, "html.parser").text)
>>> docs = loader.load()
>>> docs[0]
Document(page_content="Hello and Welcome to....)
### Expected behavior
The RecursiveUrlLoader need to have an implicit user-Agent defined with the session like in WebBaseLoader(https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/document_loaders/web_base.py) else some of the websites we are trying to scrape gives a forbidden error.
Troubleshooting the error took a lot of time and finally we realised that it was due to the lack of appropriate headers.
|
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| 1,932,250,317 |
I_kwDOIPDwls5zK8zN
| 11,539 |
The ElasticsearchStore implementation is not correct.
|
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open
| false | null |
[] | null | 8 | 2023-10-09T03:43:54 | 2024-01-12T19:28:34 | null |
NONE
| null |
### System Info
langchain==0.0.310
### Who can help?
@joemcelroy ：It seems that the method _select_relevance_score_fn was forgotten to be implemented in vectorstores-elasticsearch.
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
elastic_vector_search = ElasticsearchStore(
index_name='langchain-demo',
es_connection=es,
embedding=embedding,
)
retriever = elastic_vector_search.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={'score_threshold': 0.8})
error log：
line 268, in similarity_search_with_relevance_scores
docs_and_similarities = self._similarity_search_with_relevance_scores(
File "/usr/local/lib/python3.10/site-packages/langchain/schema/vectorstore.py", line 242, in _similarity_search_with_relevance_scores
relevance_score_fn = self._select_relevance_score_fn()
File "/usr/local/lib/python3.10/site-packages/langchain/schema/vectorstore.py", line 211, in _select_relevance_score_fn
raise NotImplementedError
### Expected behavior
I hope to implement the method and submit the changes as soon as possible. Thank you for your understanding.
|
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| 1,932,202,481 |
I_kwDOIPDwls5zKxHx
| 11,538 |
Integrating Twitter search API as a tool
|
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[] | null | 3 | 2023-10-09T02:28:26 | 2023-12-11T03:16:11 | 2023-12-11T03:16:11 |
CONTRIBUTOR
| null |
### Feature request
We are seeking to add a tool to Langchain for Twitter API (v1.1). Integrating the API as a tool will allow agents to search for tweets and timelines using a specific search query that filters by users, locations, hashtags, etc. to respond to prompts.
### Motivation
Although Langchain currently has TwitterTweetLoader, we have noticed a plethora of more parameters the Twitter API provides that are not integrated into Langchain. TwitterTweetLoader currently only allows us to specify a list of users to return tweets from and the maximum number of tweets. It would be beneficial if we had more options to specify [different operators in search queries](https://developer.twitter.com/en/docs/twitter-api/v1/rules-and-filtering/search-operators). A tool also allows an agent to actively use the API to respond to prompts, without the user having to manually create their own custom tool or load tweets manually.
### Your contribution
We have a small team of developers who will be working on this feature request, and we will submit a pull request later in 1-2 months which implements it. We will do our best to follow the guidelines for contributions, as stated in contributing.md.
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| 1,932,080,612 |
PR_kwDOIPDwls5cNQ3x
| 11,536 |
fix comments in output format
|
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[] | null | 2 | 2023-10-08T21:54:34 | 2023-10-09T21:55:45 | 2023-10-09T21:55:45 |
CONTRIBUTOR
| null |
- **Description:** Fixes the comments in the ConvoOutputParser. Because the \\\\ is escaping a single \\, they render something like: `"action_input": string \ The input to the action` in the prompt. Changing this to \\\\\\\\ lets it escape two slashes so that it renders a proper comment: `"action_input": string \\ The input to the action`
- **Issue:** N/A
- **Dependencies:**
- **Tag maintainer:** @hwchase17
- **Twitter handle:**
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PR_kwDOIPDwls5cNPvq
| 11,535 |
Fix: Missing DuckDuckGo package version
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[] | null | 2 | 2023-10-08T21:32:53 | 2023-10-09T17:55:47 | 2023-10-09T17:55:46 |
CONTRIBUTOR
| null |
[The `duckduckgo-search` v3.9.2 was removed from PyPi](https://pypi.org/project/duckduckgo-search/#history). That breaks the build.
- **Description:** refreshes the Poetry dependency to v3.9.3
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ashvardanian
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I_kwDOIPDwls5zKKLj
| 11,533 |
Add DGraqh integration with LangChain
|
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[] | null | 1 | 2023-10-08T19:46:26 | 2023-10-08T19:48:57 | null |
NONE
| null |
### Feature request
Create a natural language interface with DGraph, enabling LangChain to connect more effectively with clients using DGraph, and the DGraph community overall.
### Motivation
LangChain already boasts a range of graph integrations with databases like Neo4j and AWS Neptune. Including DGraph in LangChain's roster of graph database integrations will significantly enhance LangChain's compatibility with a wide array of software engineering projects that rely on DGraph, strengthening LangChain's position as a versatile LLM solution's provider for the DGraph-powered ecosystem.
### Your contribution
We will be looking to submit a pull request by the end of November that will contain the required code additions along with the requirements as per CONTRIBUTING.MD (involving adding a demo notebook in docs/modules and adding unit and integration tests).
|
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I_kwDOIPDwls5zKGke
| 11,532 |
Add support for a Pandas DataFrame OutputParser
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[] | null | 1 | 2023-10-08T19:22:47 | 2023-11-30T03:46:06 | 2023-11-30T03:46:06 |
CONTRIBUTOR
| null |
### Feature request
Integrating Pandas DataFrame as an output parser in LangChain AI would offer users a specific and robust tool for data analysis and manipulation. This addition would enable users to receive AI-generated data in a structured tabular format, simplifying tasks like data cleaning, transformation, and visualization while streamlining the process of extracting insights.
### Motivation
Pandas is undeniably one of the most popular and powerful libraries in Python for data manipulation and analysis. Its widespread adoption in the data science and analytics community speaks to its versatility and efficiency. Pandas simplifies tasks such as data cleaning, transformation, and exploration with easy-to-use data structures and functions. Given its frequent use in various data-related tasks, integrating Pandas DataFrames as an output parser in LangChain AI would benefit users immensely. It would provide a familiar and reliable tool for processing and interpreting data, enhancing the utility and accessibility of data-driven workflows.
### Your contribution
We will be looking to submit a pull request by the end of November that will contain the required code additions along with the requirements as per CONTRIBUTING.MD (involving adding a demo notebook in docs/modules and adding unit tests).
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| 1,931,904,754 |
I_kwDOIPDwls5zJoby
| 11,531 |
ConversationBufferMemory returning json not formatted when chain is run
|
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[] | null | 2 | 2023-10-08T16:41:12 | 2023-10-08T18:12:02 | null |
NONE
| null |
### System Info
latest
### 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
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
template = """SYSTEM:Please give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually.
Current conversation:
{history}
USER: {input}
ASSISTANT: """
PROMPT = PromptTemplate(template=template, input_variables=["history","input"])
memory = ConversationBufferMemory(memory_key='history', ai_prefix="ASSISTANT:", return_messages=True)
llmChain = ConversationChain(llm=llm, prompt=PROMPT,verbose=True, memory=memory)
AI_response = llmChain.predict(input=prompt)
print(AI_response)
#### OUTPUT:
Entering new ConversationChain chain...
Prompt after formatting:
SYSTEM: Please give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually.
Current conversation:
*** [HumanMessage(content='hello'), AIMessage(content=' Hello! How can I assist you today?')] ***
USER: who was president in 1920?
ASSISTANT:
RETURNING JSON OBJECT instead of formatted string.
### Expected behavior
https://python.langchain.com/docs/modules/memory/conversational_customization
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Friend: Hi there!
AI: Hi there! It's nice to meet you. How can I help you today?
Friend: What's the weather?
AI:
> Finished ConversationChain chain.
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PR_kwDOIPDwls5cMm8y
| 11,530 |
fix wording of query_sql_database_tool_description
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[] | null | 2 | 2023-10-08T16:15:17 | 2023-10-09T22:32:46 | 2023-10-09T22:32:46 |
CONTRIBUTOR
| null |
- **Description:** Fixes minor typo for the query_sql_database_tool_description in the db toolkit
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @nfcampos
- **Twitter handle:** N/A
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PR_kwDOIPDwls5cMjVf
| 11,529 |
Update to doc Pydantic example to Pydantic 2
|
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[] | null | 2 | 2023-10-08T15:37:17 | 2023-11-02T00:31:22 | 2023-11-02T00:31:21 |
CONTRIBUTOR
| null |
**Description:**
Currently, the example uses Pydantic 1 format and crashes with the following error:
```
452 if isinstance(info, ValidatorDecoratorInfo):
--> 453 res.validators[var_name] = Decorator.build(
454 model_dc, cls_var_name=var_name, shim=var_value.shim, info=info
455 )
456 elif isinstance(info, FieldValidatorDecoratorInfo):
457 res.field_validators[var_name] = Decorator.build(
458 model_dc, cls_var_name=var_name, shim=var_value.shim, info=info
...
83 needs_values_kw = True
PydanticUserError: The `field` and `config` parameters are not available in Pydantic V2, please use the `info` parameter instead.
```
Additionally, it produces a warning:
```
PydanticDeprecatedSince20: Pydantic V1 style `@validator` validators are deprecated. You should migrate to Pydantic V2 style `@field_validator` validators, see the migration guide for more details. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.4/migration/
@validator('setup')
```
------
Here I'm updating it to use Pydantic2 syntax
|
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| 1,931,867,256 |
PR_kwDOIPDwls5cMjEo
| 11,528 |
fix: PromptTemplate with OutputParser documentation not working
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[] | null | 4 | 2023-10-08T15:35:10 | 2023-11-02T00:37:17 | 2023-11-02T00:37:16 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** [PromptTemplate with OutputParser](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization#prompttemplate-with-outputparser) has an example with "template": `"Given the following question and student answer, provide a correct answer and score the student answer.\nQuestion: {question}\nStudent Answer: {student_answer}\nCorrect Answer:"`, however, `\n` is a control character therefore `load_prompt ` results in an error like `ERROR Invalid control character at: line 1 column 202 (char 202)`. This is expected behavior from the `json` package (See https://stackoverflow.com/questions/22394235/invalid-control-character-with-python-json-loads for more information). This PR removes these control characters. I've confirmed that the fixed example is working on Google Colab .
- **Issue:** Not found,
- **Dependencies:** Nope,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** @dosuken123
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_kwDOIPDwls5cMc93
| 11,527 |
feat: add batch_loader class
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CONTRIBUTOR
| null |
- **Description:** add a batch loader to better use loader on a list of data sources
- **Issue:** #11509
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PR_kwDOIPDwls5cMObX
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Bos loader
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[] | null | 2 | 2023-10-08T11:17:48 | 2023-11-03T11:01:26 | 2023-10-11T21:43:48 |
CONTRIBUTOR
| null |
**Description:**
Add BaiduCloud BOS document loader.
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I_kwDOIPDwls5zI1qg
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documents have error (create_structured_output_chain)
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NONE
| null |
### Issue with current documentation:
https://python.langchain.com/docs/modules/chains/how_to/openai_functions
from typing import Sequence
class Person(BaseModel):
"""Identifying information about a person."""
name: str = Field(..., description="The person's name")
age: int = Field(..., description="The person's age")
fav_food: Optional[str] = Field(None, description="The person's favorite food")
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a world class algorithm for extracting information in structured formats."),
("human", "Use the given format to extract information from the following input: {input}"),
("human", "Tip: Make sure to answer in the correct format"),
]
)
class People(BaseModel):
"""Identifying information about all people in a text."""
people: Sequence[Person] = Field(..., description="The people in the text")
chain = create_structured_output_chain(People, llm, prompt, verbose=True)
chain.run(
"Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally."
)
no work,having some error:
ValidationError: 1 validation error for _OutputFormatter
output
value is not a valid dict (type=type_error.dict)
### Idea or request for content:
_No response_
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| 1,931,680,189 |
PR_kwDOIPDwls5cL9GR
| 11,523 |
Docs: Fix broken line breaks in snippets
|
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[] | null | 2 | 2023-10-08T07:11:48 | 2023-10-09T22:40:28 | 2023-10-09T22:40:27 |
CONTRIBUTOR
| null |
**Description:**
This PR fix some code snippets that have raw `\n`'s instead of actual line breaks.
**Issue:**
Currently some snippets look like this:
Affected pages:
- https://python.langchain.com/docs/integrations/providers/predictionguard#example-usage
- https://python.langchain.com/docs/modules/agents/how_to/custom_llm_agent#set-up-environment
- https://python.langchain.com/docs/modules/chains/foundational/llm_chain#get-started
- https://python.langchain.com/docs/integrations/providers/shaleprotocol#how-to
**Tag maintainer:**
@hwchase17
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PR_kwDOIPDwls5cLtoT
| 11,522 |
Fixed a bug in reporting Python code validation
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[] | null | 2 | 2023-10-08T02:26:24 | 2023-10-11T21:34:28 | 2023-10-11T21:34:28 |
CONTRIBUTOR
| null |
- **Description:** fixed a bug in pal-chain when it reports Python
code validation errors. When node.func does not have any ids, the
original code tried to print node.func.id in raising ValueError.
- **Issue:** n/a,
- **Dependencies:** no dependencies,
- **Tag maintainer:** @hazzel-cn, @eyurtsev
- **Twitter handle:** @lazyswamp
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PR_kwDOIPDwls5cLp7M
| 11,521 |
RunnableLambda: Add in code docs
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[] | null | 1 | 2023-10-08T01:19:15 | 2023-10-09T18:37:48 | 2023-10-09T18:37:47 |
COLLABORATOR
| null |
Add in code docs for Runnable Lambda
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PR_kwDOIPDwls5cLogX
| 11,520 |
Add runnables to api reference
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[] | null | 1 | 2023-10-08T00:53:54 | 2023-10-17T15:46:10 | 2023-10-17T15:46:09 |
COLLABORATOR
| null |
Need to look at preview whether this works.
|
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11474
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[] | null | 4 | 2023-10-08T00:14:05 | 2023-11-13T03:49:11 | 2023-10-11T23:30:16 |
CONTRIBUTOR
| null |
No relevant documents may be found for a given question. In some use cases, we could directly respond with a fixed message instead of doing an LLM call with an empty context. This PR exposes this as an option: response_if_no_docs_found.
|
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PR_kwDOIPDwls5cLhqL
| 11,518 |
implemented add files method in LLMRails
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[] | null | 3 | 2023-10-07T23:10:03 | 2023-10-09T23:29:43 | 2023-10-09T23:29:43 |
CONTRIBUTOR
| null |
This PR provides add files method with LLMRails. Implemented here are:
docs/extras/integrations/vectorstores/llm-rails.ipynb
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Llmrails add files update
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[] | null | 1 | 2023-10-07T23:02:59 | 2023-10-07T23:04:11 | 2023-10-07T23:04:11 |
CONTRIBUTOR
| null |
This PR provides add files method with LLMRails. Implemented here are:
docs/extras/integrations/vectorstores/llm-rails.ipynb
|
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PR_kwDOIPDwls5cLV4E
| 11,516 |
Add documentation to Runnable
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[] | null | 1 | 2023-10-07T19:59:48 | 2023-10-08T07:09:05 | 2023-10-08T07:09:04 |
COLLABORATOR
| null |
Add in code documentation for runnable.
|
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I_kwDOIPDwls5zH68_
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Neo4jVectorstore: Parse vectorstore specific parameters in **kwargs when calling add_embeddings()
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[] | null | 2 | 2023-10-07T18:29:54 | 2023-10-07T22:17:39 | null |
NONE
| null |
I am trying to use the `add_texts` method on a previously instanciated Neo4jVector object for setting the node_label.
The scenario is that I am processing different types of text and want to create different nodes accordingly. Because I am handling the database connection somewhere else in my code, I don't want to establish the connection to the database when embedding texts.
```python
[...]
# initialize vector store
self.vector_store = Neo4jVector(
embedding=self.embeddings_model,
username=neo4j_config['USER'],
password=neo4j_config['PASSWORD'],
url=neo4j_config['URI'],
database=neo4j_config['DATABASE'], # neo4j by default
index_name="vector", # vector by default
embedding_node_property="embedding", # embedding by default
create_id_index=True, # True by default
)
[...]
# embed text
created_node = self.vector_store.add_texts(
[text],
metadatas=[
{
"label": [node_label]
}
],
embedding=self.embeddings_model,
node_label=node_label, # Chunk by default
text_node_property=text_node_property, # text by default
)
```
Currently the `node_label` property is overwritten with the default value and not respected when used as a parameter in **kwargs.
https://github.com/langchain-ai/langchain/blob/eb572f41a65c9636b9e0e5a5fb4210a00a67a353/libs/langchain/langchain/vectorstores/neo4j_vector.py#L125
https://github.com/langchain-ai/langchain/blob/eb572f41a65c9636b9e0e5a5fb4210a00a67a353/libs/langchain/langchain/vectorstores/neo4j_vector.py#L450
According to the Docstring, the function is supposed to accept vectorstore specific parameters:
https://github.com/langchain-ai/langchain/blob/eb572f41a65c9636b9e0e5a5fb4210a00a67a353/libs/langchain/langchain/vectorstores/neo4j_vector.py#L458
In order to conform with this, I added this function:
```python
def update_vector_store_properties(self, **kwargs):
# List of vector store-specific arguments we want to check and potentially update
vector_store_args = ["node_label", "text_node_property", "embedding_node_property"]
# Iterate over these arguments
for arg in vector_store_args:
# Check if the argument is present in kwargs
if arg in kwargs:
# Update the corresponding property of the Neo4jVector instance
setattr(self, arg, kwargs[arg])
```
I then modified `add_embeddings` like this:
```python
def add_embeddings(
self,
texts: Iterable[str],
embeddings: List[List[float]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Add embeddings to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
embeddings: List of list of embedding vectors.
metadatas: List of metadatas associated with the texts.
kwargs: vectorstore specific parameters
"""
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
if not metadatas:
metadatas = [{} for _ in texts]
self.update_vector_store_properties(**kwargs)
[...]
```
Don't really know if this is a missing feature or a bug or if I am just using the Vectorstore wrong, but I thought I'd leave this here in case it might be the desired behavior.
|
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PR_kwDOIPDwls5cLOSr
| 11,514 |
refactor: enable connection pool usage in PGVector
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CONTRIBUTOR
| null |
- **Description:** `PGVector` refactored to use connection pool.
- **Issue:** #11433,
- **Tag maintainer:** @hwchase17 @eyurtsev,
|
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PR_kwDOIPDwls5cLD0d
| 11,513 |
Google Scholar Search Tool using serpapi
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[] | null | 8 | 2023-10-07T15:34:08 | 2023-12-16T20:28:31 | 2023-10-20T21:35:55 |
CONTRIBUTOR
| null |
- **Description:** Implementing the Google Scholar Tool as requested in PR #11505. The tool will be using the [serpapi python package](https://serpapi.com/integrations/python#search-google-scholar). The main idea of the tool will be to return the results from a Google Scholar search given a query as an input to the tool.
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17
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PR_kwDOIPDwls5cLBgg
| 11,512 |
Add option to specify document metadata in python CSVLoader
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closed
| false | null |
[] | null | 1 | 2023-10-07T14:59:09 | 2023-10-09T20:45:22 | 2023-10-09T20:45:11 |
CONTRIBUTOR
| null |
#### Description
This PR adds the option to specify additional metadata columns in the CSVLoader beyond just `Source`.
The current CSV loader includes all columns in `page_content` and if we want to have columns specified for `page_content` and `metadata` we have to do something like the below.:
```
csv = pd.read_csv(
"path_to_csv"
).to_dict("records")
documents = [
Document(
page_content=doc["content"],
metadata={
"last_modified_by": doc["last_modified_by"],
"point_of_contact": doc["point_of_contact"],
}
) for doc in csv
]
```
#### Usage
Example Usage:
```
csv_test = CSVLoader(
file_path="path_to_csv",
metadata_columns=["last_modified_by", "point_of_contact"]
)
```
Example CSV:
```
content, last_modified_by, point_of_contact
"hello world", "Person A", "Person B"
```
Example Result:
```
Document {
page_content: "hello world"
metadata: {
row: '0',
source: 'path_to_csv',
last_modified_by: 'Person A',
point_of_contact: 'Person B',
}
```
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| 1,931,340,159 |
PR_kwDOIPDwls5cK5O_
| 11,511 |
Rspace doc loader
|
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[] | null | 4 | 2023-10-07T12:49:37 | 2023-10-13T09:16:14 | 2023-10-12T22:05:39 |
CONTRIBUTOR
| null |
**Description:**
Add a document loader for the RSpace Electronic Lab Notebook (www.researchspace.com), so that scientific documents and research notes can be easily pulled into Langchain pipelines.
**Issue**
This is an new contribution, rather than an issue fix.
**Dependencies:**
There are no new required dependencies.
In order to use the loader, clients will need to install rspace_client SDK using `pip install rspace_client`
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I_kwDOIPDwls5zHXay
| 11,510 |
Can't import UnstructuredRSTLoader
|
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[] | null | 1 | 2023-10-07T11:17:32 | 2023-10-07T11:33:42 | 2023-10-07T11:33:42 |
NONE
| null |
### System Info
Langchain 0.0.310
Python 3.11
Windows 10
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just following the usual steps:
`from langchain.document_loaders import UnstructuredRSTLoader`
### Expected behavior
Expecting a successful import but greeted with
`ImportError: cannot import name 'Document' from 'langchain.schema' `
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I_kwDOIPDwls5zHQdT
| 11,509 |
[Feature] Batch loader
|
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[] | null | 1 | 2023-10-07T09:48:11 | 2023-10-08T15:00:37 | null |
CONTRIBUTOR
| null |
### Feature request
Loaders are designed to load a single source of data and transform it to a list of Documents. If several sources are to be processed, it is up to the developer to call the Loader several times.
The idea is to propose a new `BatchLoader` object that could simplify this task and enable loading tasks to be launched sequentially, mutlithreaded, with mutli processor or asynchronously.
Draft idea:
```
class BatchLoader(BaseLoader):
...
def load(self, method: str = 'sequential', max_workers: int=1) -> List[Document]:
# TODO: faire ça stylé
if method == 'thread':
return self._load_thread(max_workers)
elif method == 'process':
return self._load_process(max_workers)
elif method == 'sequential':
return self._load_sequential()
elif method == 'async':
return asyncio.run(self._async_load())
else:
raise ValueError(f'Invalid method {method}')
...
# Call it with a Loader constructor callable and a set of arguments
batch_loader = BatchLoader(TextLoader, {"file_path": [f"file_{i}.txt" for i in range(1000)]})
```
### Motivation
In most real use case we need to load several Data Sources, combine their outputs and work with them (e.g. store in a vector store). Some use cases also require to wait for I/O intensive tasks or tasks you want to parallelized, so having a Meta loader that wrap this complexity for you could be a good idea.
### Your contribution
WIP: https://github.com/langchain-ai/langchain/pull/11527
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| 1,931,176,253 |
I_kwDOIPDwls5zG2k9
| 11,508 |
why when streaming only setup n=1
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[] | null | 1 | 2023-10-07T04:08:32 | 2023-10-07T04:14:27 | null |
NONE
| null |
### Issue you'd like to raise.
I want streaming when call chatgpt with n>1. can u support me?
### Suggestion:
_No response_
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PR_kwDOIPDwls5cKR3C
| 11,507 |
Fixed typos in SmartLLMChain ideation and critique prompts
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[] | null | 2 | 2023-10-07T02:37:59 | 2023-11-29T03:05:19 | null |
NONE
| null |
Noticed and fixed a few typos in the SmartLLMChain default ideation and critique prompts
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PR_kwDOIPDwls5cKOqE
| 11,506 |
Resolved: Revert "Add ChatGLM for llm and chat_model by using ChatGLM API (#9797)" #10805
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[] | null | 3 | 2023-10-07T02:07:21 | 2023-11-16T07:10:28 | null |
CONTRIBUTOR
| null |
Resolved: Revert "Add ChatGLM for llm and chat_model by using ChatGLM API (#9797)" #10805
|
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I_kwDOIPDwls5zGXxk
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Add support for google scholar querying with serpapi
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[] | null | 4 | 2023-10-06T23:45:22 | 2023-11-13T20:13:57 | 2023-11-13T20:13:56 |
CONTRIBUTOR
| null |
### Feature request
We propose the addition of Google Scholar querying with the serpapi, as google Scholar is a prominent platform for accessing academic and scholarly articles.
### Motivation
Google scholar is a crucial resource for researchers, students, and professionals in various fields. Integrating Google Scholar querying with serpapi would provide users with the ability to programmatically retrieve search results from Google Scholar, giving the llms more better/more reliable information to work with.
### Your contribution
We intend to submit a pull request for this issue at some point in November.
|
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PR_kwDOIPDwls5cJ0_B
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QoL improvements to query constructor
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[] | null | 1 | 2023-10-06T22:37:28 | 2023-10-09T15:10:53 | 2023-10-09T15:10:52 |
COLLABORATOR
| null |
updating query constructor and self query retriever to
- make it easier to pass in examples
- validate attributes used in query
- remove invalid parts of query
- make it easier to get + edit prompt
- make query constructor a runnable
- make self query retriever use as runnable
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PR_kwDOIPDwls5cJwzA
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wrap confluence attachment processing with a try-except block
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[] | null | 2 | 2023-10-06T22:12:45 | 2023-10-12T01:13:42 | 2023-10-12T01:13:42 |
CONTRIBUTOR
| null |
Prevents document loading from erroring out when an attachment is not found at the url.
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PR_kwDOIPDwls5cJut2
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docs: update `dependents`
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[] | null | 1 | 2023-10-06T22:02:32 | 2023-10-10T16:58:04 | 2023-10-10T16:31:24 |
COLLABORATOR
| null |
A regular update of dependents.
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PR_kwDOIPDwls5cJtls
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feat: Add BigFramesLLM and BigFramesChains for batch prompts cases
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[] | null | 1 | 2023-10-06T21:56:00 | 2023-10-16T21:18:30 | null |
CONTRIBUTOR
| null |
This PR adds [Bigframes (BigQuery DataFrames)](https://cloud.google.com/python/docs/reference/bigframes/latest) integration to Langchain. BigQuery DataFrames provides a Pythonic DataFrame and machine learning (ML) API powered by the BigQuery engine. BigFrames supports manipulating data in the DataFrame to build LLM prompts, and sending DataFrame prompts to the LLM model using [text-bison model of the PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text)
Using BigFramesChain, you can now batch prompts using DataFrames as input and output.
<!-- 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_kwDOIPDwls5zGCQN
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Retry limit is not respected
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[] | null | 1 | 2023-10-06T21:37:53 | 2023-10-06T21:44:16 | null |
NONE
| null |
### System Info
Langchain==0.0305
Mac
python == 3.9.6
### 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 have a simple web page summarize function basacilly followed example
```python
llm = OpenAI(temperature=0)
loader = WebBaseLoader(link)
docs = loader.load()
MAP_PROMPT = PromptTemplate(
template="""Write a concise summary of the following web page(keep all identifiable information about the main entities):
{text}
CONCISE SUMMARY:
""",
input_variables=["text"],
)
REDUCE_PROMPT = PromptTemplate(
template="""Based on the following text, write a concise summary(keep all identifiable information about the main entities)):
{text}
CONCISE SUMMARY:
""",
input_variables=["text"],
)
chain = load_summarize_chain(
llm,
chain_type="map_reduce",
return_intermediate_steps=True,
map_prompt=MAP_PROMPT,
combine_prompt=REDUCE_PROMPT,
)
text_splitter = RecursiveCharacterTextSplitter()
docs = text_splitter.split_documents(docs)
if len(docs) == 0:
return ""
result = chain(
text_splitter.split_documents(documents=docs),
return_only_outputs=True,
)
logging.debug(result)
return result["output_text"]
```
my program loaded in a pdf url link (https://www.fiserv.com/content/dam/fiserv-ent/final-files/marketing-collateral/case-studies/regions-bank-case-study-0623.pdf)
web based loader created 1 page document with a large number of tokens (13524539 characters)
I then started to get openai library error:
```
error_code=rate_limit_exceeded error_message='Rate limit reached for default-text-davinci-003 in organization org-a6hHivOPvKe9X60G8E6YqM5m on tokens_usage_based per min. Limit: 250000 / min. Current: 245411 / min. Contact us through our help center at help.openai.com if you continue to have issues.' error_param=None error_type=tokens_usage_based message='OpenAI API error received' stream_error=False
```
langchain kept retrying on this error definetly more than the default 6 times, I had `14` retry log from langchain in my terminal.
I think this is a quite critical bug, my OpenAI usage limit blew up the $120 limit under 10mins because of this.
### Expected behavior
retry limit should kick it to short circuit this situation or there should be some other method to prevent unexpected length of document being passed to openAI
|
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PR_kwDOIPDwls5cJnmO
| 11,499 |
Fetch up-to-date attributes for env-pulled kwargs during serialisation of OpenAI classes
|
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[] | null | 1 | 2023-10-06T21:23:47 | 2023-10-06T21:43:30 | 2023-10-06T21:43:29 |
CONTRIBUTOR
| null |
These attributes can be filled by an environment variable, but are not marked as a secret. Thus, when invoking serialisation, the values are not included in the output JSON.
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Make example work during pydantic transition
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[] | null | 3 | 2023-10-06T21:23:21 | 2023-10-12T01:44:55 | 2023-10-12T01:44:47 |
CONTRIBUTOR
| null |
**Description:**
Make the example extraction code on https://python.langchain.com/docs/use_cases/extraction work again by importing the langchain.pydantic_v1 lib instead of the v2.
**Issue:**
Solves issue https://github.com/langchain-ai/langchain/issues/11468
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Langsmith in readme update
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I_kwDOIPDwls5zFy7D
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Redis Vectorestore.add_documents() should use embed_documents() instead of embed_query()
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[] | null | 2 | 2023-10-06T20:31:55 | 2023-11-06T22:17:18 | null |
CONTRIBUTOR
| null |
### Feature request
The Redis Vectorstore `add_documents()` method calls `add_texts()` which embeds documents one by one like:
```
embedding = (
embeddings[i] if embeddings else self._embeddings.embed_query(text)
)
```
The `add_documents()` method would seem to imply that it is a good way to jointly embed and upload a larger list of documents, but using an API call for each document as above is slow and prone to run into rate limits.
Redis `add_documents()` could pass a kwarg `from_documents=True`to `add_texts()` which would change embedding to
```
embedding = (
embeddings[i] if embeddings else self._embeddings.embed_documents(documents)
)
```
### Motivation
This would simplify the overall use of the Redis Vectorstore and would be more inline with how the example documents imply the add_documents() method should be used. Currently `add_documents()` is not suitable for something like a csv or a long list of smaller documents unless it is used like:
```
add_documents(
documents=split_documents,
embeddings=embedder.embed_documents(texts),
)
```
### Your contribution
I could create a PR for this issue if it makes sense!
|
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raise openapi import error
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PR_kwDOIPDwls5cJIkH
| 11,494 |
Rename some last hwchase17/langchain links
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[] | null | 1 | 2023-10-06T19:12:23 | 2023-10-06T19:34:31 | 2023-10-06T19:34:30 |
COLLABORATOR
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| 1,930,790,376 |
PR_kwDOIPDwls5cJEjz
| 11,493 |
Remove optional from vectara source
|
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[] | null | 1 | 2023-10-06T18:56:29 | 2023-10-06T19:12:44 | 2023-10-06T19:12:44 |
COLLABORATOR
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fyi @ofermend
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I_kwDOIPDwls5zFAKJ
| 11,492 |
await PGVectorStore throws an ts(1378) error.
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[] | null | 1 | 2023-10-06T17:37:48 | 2023-10-06T17:42:55 | null |
NONE
| null |
### System Info
MacOS, PGVector, TypeScript, NodeJS.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hey!
I have been trying to reproduce the langchain's pgvector official documentation without success. For some reason when I try to use async await around PGVector it throws an error.
`const pgvectorStore = await PGVectorStore.initialize(
new OpenAIEmbeddings(),
config
);`
This is the official documentation.
`export const pgvectorStore = await PGVectorStore.initialize(
new OpenAIEmbeddings(),
config
);`
This is my version.
I'm using TypeScript and it recommends to change both module and target versions inside compilerOptions, **done both without success.**
`Top-level 'await' expressions are only allowed when the 'module' option is set to 'es2022', 'esnext', 'system', 'node16', or 'nodenext', and the 'target' option is set to 'es2017' or higher.ts(1378)`
This is the output if I try to run the code:
`node:internal/process/esm_loader:108
internalBinding('errors').triggerUncaughtException(
^
Error: Transform failed with 1 error:
/Users/diogo/Documents/www/ai-api/src/store.ts:34:29: ERROR: Top-level await is currently not supported with the "cjs" output format
at failureErrorWithLog (/Users/diogo/.npm/_npx/fd45a72a545557e9/node_modules/esbuild/lib/main.js:1649:15)
at /Users/diogo/.npm/_npx/fd45a72a545557e9/node_modules/esbuild/lib/main.js:847:29
at responseCallbacks.<computed> (/Users/diogo/.npm/_npx/fd45a72a545557e9/node_modules/esbuild/lib/main.js:703:9)
at handleIncomingPacket (/Users/diogo/.npm/_npx/fd45a72a545557e9/node_modules/esbuild/lib/main.js:762:9)
at Socket.readFromStdout (/Users/diogo/.npm/_npx/fd45a72a545557e9/node_modules/esbuild/lib/main.js:679:7)
at Socket.emit (node:events:517:28)
at addChunk (node:internal/streams/readable:335:12)
at readableAddChunk (node:internal/streams/readable:308:9)
at Readable.push (node:internal/streams/readable:245:10)
at Pipe.onStreamRead (node:internal/stream_base_commons:190:23) {
errors: [
{
detail: undefined,
id: '',
location: {
column: 29,
file: '/Users/diogo/Documents/www/ai-api/src/store.ts',
length: 5,
line: 34,
lineText: 'export const pgvectorStore = await PGVectorStore.initialize(',
namespace: '',
suggestion: ''
},
notes: [],
pluginName: '',
text: 'Top-level await is currently not supported with the "cjs" output format'
}
],
warnings: []
}_`
Would appreciate any help.
### Expected behavior
It should not throw an error.
|
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| 1,930,672,307 |
PR_kwDOIPDwls5cIriX
| 11,491 |
Fix Open in Colab link for ClearML docs 2
|
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[] | null | 2 | 2023-10-06T17:24:42 | 2023-10-06T19:01:48 | 2023-10-06T19:01:48 |
CONTRIBUTOR
| null |
Description: Fixed the Open in Colab link for ClearML docs
Issue: https://github.com/allegroai/clearml/issues/1125
Twitter handle: DziezycMaciej
|
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