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| 1,912,526,628 |
PR_kwDOIPDwls5bLdBK
| 11,044 |
add tracer
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[] | null | 1 | 2023-09-26T01:32:22 | 2023-09-26T02:45:08 | 2023-09-26T02:45:08 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5bLZ-W
| 11,043 |
Update LangSmith Walkthrough
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[] | null | 1 | 2023-09-26T01:11:10 | 2023-09-26T05:32:57 | 2023-09-26T05:32:57 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5x_hm0
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Issue with Sagemaker
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[] | null | 2 | 2023-09-26T00:27:24 | 2023-12-26T16:01:26 | null |
NONE
| null |
### System Info
python3
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
import boto3
import json
PROFILE_NAME = 'PROFILE'
REGION_NAME = 'REGION'
ENDPOINT_NAME = 'ENDPOINT'
CONTENT_TYPE = 'application/json'
CUSTOM_ATTRIBUTES = 'accept_eula=true'
TEMPERATURE = 0.001
MAX_LENGTH = 1000
def build_llama2_prompt(message):
startPrompt = "<s>[INST] "
endPrompt = " [/INST]"
conversation = []
if message["role"] == "system":
conversation.append(f"<<SYS>>\n{message['content']}\n<</SYS>>\n\n")
elif message["role"] == "user":
conversation.append(message["content"].strip())
else:
conversation.append(f" [/INST] {message['content'].strip()}</s><s>[INST] ")
return startPrompt + "".join(conversation) + endPrompt
def transform_input(prompt):
payload = {
"inputs": [
[
{"role": "user", "content": prompt},
],
],
"parameters": {"max_new_tokens": 1000, "top_p": 0.6, "temperature": 0.1},
}
input_str = json.dumps(
payload,
)
return input_str
def invoke_sagemaker_endpoint(profile_name, region_name, endpoint_name, content_type, custom_attributes, payload):
session = boto3.Session(profile_name=profile_name, region_name=region_name)
sagemaker = session.client('sagemaker-runtime')
response = sagemaker.invoke_endpoint(
EndpointName=endpoint_name,
ContentType=content_type,
Body=json.dumps(payload),
CustomAttributes=custom_attributes
)
body = response['Body']
prediction_result = json.loads(body.read().decode('utf-8'))
return prediction_result
def pretty_print_result(result):
text = result[0]['generated_text']
print(f"{ANSI_GREEN}Prediction Result:{ANSI_RESET}")
print(f"{ANSI_GREEN}{text}{ANSI_RESET}")
if __name__ == "__main__":
input_text = 'String'
print("prompt")
print(input_text)
prompt = transform_input(input_text)
request_payload = {
'inputs': prompt,
'parameters': {
"do_sample": True,
"top_p": 0.6,
"temperature": 0.9,
"top_k": 50,
"max_new_tokens": 512,
"repetition_penalty": 1.03,
"stop": ["</s>"]
}
}
prediction_result = invoke_sagemaker_endpoint(PROFILE_NAME, REGION_NAME, ENDPOINT_NAME, CONTENT_TYPE, CUSTOM_ATTRIBUTES, request_payload)
pretty_print_result(prediction_result)
```
### Expected behavior
bugging, leading to error:
` raise error_class(parsed_response, operation_name)
botocore.errorfactory.ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (424) from primary with message "{
"code":424,
"message":"prediction failure",
"error":"string indices must be integers"
`
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PR_kwDOIPDwls5bLNfA
| 11,040 |
Support extra tools argument for pandas agent toolkit
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closed
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[] | null | 1 | 2023-09-25T23:49:30 | 2023-09-27T17:57:05 | 2023-09-27T17:57:05 |
CONTRIBUTOR
| null |
**Description**
We support adding new tools in some toolkits already like the [SQLAgent toolkit](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/agents/agent_toolkits/sql/base.py#L27).
Related [SO](https://stackoverflow.com/questions/76583163/are-langchain-toolkits-able-to-be-modified-can-we-add-tools-to-a-pandas-datafra) thread
This replicates the same functionality here, so users can add custom bespoke tools.
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PR_kwDOIPDwls5bLLgm
| 11,039 |
add HTMLHeaderTextSplitter
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[] | null | 5 | 2023-09-25T23:39:29 | 2023-10-04T13:29:16 | 2023-10-04T13:24:25 |
CONTRIBUTOR
| null |
Description: Similar in concept to the `MarkdownHeaderTextSplitter`, the `HTMLHeaderTextSplitter` is a "structure-aware" chunker that splits text at the element level and adds metadata for each header "relevant" to any given chunk. It can return chunks element by element or combine elements with the same metadata, with the objectives of (a) keeping related text grouped (more or less) semantically and (b) preserving context-rich information encoded in document structures. It can be used with other text splitters as part of a chunking pipeline.
Dependency: lxml python package
Maintainer: @hwchase17
Twitter handle: @MartinZirulnik
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PR_kwDOIPDwls5bLLWR
| 11,038 |
`LlamaCppEmbeddings`: adds `verbose` parameter, similar to `llms.LlamaCpp` class
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[] | null | 3 | 2023-09-25T23:38:31 | 2023-09-29T01:45:42 | 2023-09-29T01:37:51 |
CONTRIBUTOR
| null |
## Description
As of now, when instantiating and during inference, `LlamaCppEmbeddings` outputs (a lot of) verbose when controlled from Langchain binding - it is a bit annoying when computing the embeddings of long documents, for instance.
This PR adds `verbose` for `LlamaCppEmbeddings` objects to be able **not** to print the verbose of the model to `stderr`. It is natively supported by `llama-cpp-python` and directly passed to the library – the PR is hence very small.
The value of `verbose` is `True` by default, following the way it is defined in [`LlamaCpp` (`llamacpp.py` #L136-L137)](https://github.com/langchain-ai/langchain/blob/c87e9fb2ce0ae617e3b2edde52421c80adef54cc/libs/langchain/langchain/llms/llamacpp.py#L136-L137)
## Issue
_No issue linked_
## Dependencies
_No additional dependency needed_
## To see it in action
```python
from langchain.embeddings import LlamaCppEmbeddings
MODEL_PATH = "<path_to_gguf_file>"
if __name__ == "__main__":
llm_embeddings = LlamaCppEmbeddings(
model_path=MODEL_PATH,
n_gpu_layers=1,
n_batch=512,
n_ctx=2048,
f16_kv=True,
verbose=False,
)
```
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PR_kwDOIPDwls5bKwYM
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skip excluded sublinks before recursion
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[] | null | 1 | 2023-09-25T21:33:43 | 2023-09-26T09:24:56 | 2023-09-26T09:24:55 |
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I_kwDOIPDwls5x-5SI
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Call to langchain.vectorstores.pinecone.add_texts needs to use deepcopy for metadata
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[] | null | 2 | 2023-09-25T21:31:26 | 2023-12-25T16:01:44 | null |
NONE
| null |
### System Info
LangChain 0.0.295
Python 3.11.5
pinecone-client 2.2.4
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm calling add_texts to add texts and their metadata to Pinecone. Many of my texts have identical metadata ({"chapter": chapter}), so I created a metadata object and passed that in for each to metadatas as its metadata, when calling add_texts.
` # For each text, associate the chapter number as metadata
metadata = {"chapter": chapter}
# Add document embeddings to vector store first
try:
doc_ids = vector_store_service.add_texts(
doc_texts,
metadatas=[metadata for _ in doc_texts],
index_name=index_name,
namespace=namespace,
)
`
However, add_texts manipulates the items within metadatas, expecting them to be isolated objects. This includes writing a "text" metadata field.
https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html#Pinecone.add_texts
Because the texts I passed in were referencing the same metadata object that was passed in, when the LangChain code updated the "metadata" for that particular text, including adding the _text_key, that effectively overwrote the values for all of the texts that shared that same metadata object.
The result was that all texts which shared a metadata object that was input ended up having the text of the final text that was processed.
### Expected behavior
I worked around this on the client side by changing my metadatas argument so that the items in the list do not reference a shared object. It would be my expectation that inside add_texts, the function would use something like deepcopy to ensure that modifying one metadata (or text, for that matter) does not affect any of the others, which is a weird side effect.
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PR_kwDOIPDwls5bKkVv
| 11,033 |
add more import checks
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[] | null | 1 | 2023-09-25T20:48:19 | 2023-09-27T18:17:14 | 2023-09-27T18:17:13 |
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PR_kwDOIPDwls5bKhbu
| 11,032 |
Support for PDF in WebResearchRetriever (plus sources fix)
|
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[] | null | 4 | 2023-09-25T20:38:04 | 2024-01-10T01:55:35 | null |
NONE
| null |
**Description:**
- `WebResearchRetriever` would fail on PDF documents. To address this I have created a new `async_pdf` document loader and integrated with `WebResearchRetriever`. There is a lot of similarity with `async_html`, and certainly opportunity to share code via base class (or consolidate both into a single `async_web` class).
- The `async_pdf` class has its own PDF download code, rather than use `PyPDFLoader` directly to download the remote file. This is because `PyPDFLoader` does not currently support timeouts or optional SSL verify (no timeouts can result in infinite hang).
- Added a new method that inspects the content-type of the remote URL and only keeps known types. This should reduce content decoding errors
- Also added a small fix for missing sources coming from `RetrievalQAWithSourcesChain`. The regex in `_split_sources` would get confused if there is a newline between "SOURCES:" and the source strings in the answer.
**Issues:** https://github.com/langchain-ai/langchain/issues/11031, https://github.com/langchain-ai/langchain/issues/10575
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I_kwDOIPDwls5x-aG5
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WebResearchRetriever fails on PDF files
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[] | null | 3 | 2023-09-25T19:52:59 | 2023-12-26T01:07:44 | null |
NONE
| null |
### Discussed in https://github.com/langchain-ai/langchain/discussions/8630
<div type='discussions-op-text'>
<sup>Originally posted by **matthewosm** August 2, 2023</sup>
Hi I'm using WebResearchRetriever to do materials research, unfortunately the top results are often PDFs so I get the error:
`langchain.document_loaders.async_html:Failed to decode content from xyz.pdf`
I've seen there is a [PDF Loader](https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf), but I'm not quite sure how to get it to work with my script.
Here is what I have so far:
```
import faiss, logging
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chat_models import ChatOpenAI
from langchain.retrievers.web_research import WebResearchRetriever
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.docstore import InMemoryDocstore
from langchain.utilities import GoogleSearchAPIWrapper
embedding_model = OpenAIEmbeddings()
index = faiss.IndexFlatL2(1536)
vectorstore = FAISS(embedding_model.embed_query, index, InMemoryDocstore({}), {})
llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k", temperature=0.5, streaming=True)
search = GoogleSearchAPIWrapper()
web_retriever = WebResearchRetriever.from_llm(vectorstore=vectorstore, llm=llm, search=search, num_search_results=6)
question = "Define THIS MATERIAL"
if question:
logging.basicConfig()
logging.getLogger("langchain.retrievers.web_research").setLevel(logging.INFO)
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm, retriever=web_retriever)
result = qa_chain({"question": question})
answer, sources = result['answer'], result['sources']
print(answer, sources)
```</div>
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Harrison/move vectorstore base
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I_kwDOIPDwls5x-LoT
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Issue: 0.0.300 gives `Exception building prompt: predict() got an unexpected keyword argument 'stop_sequences'"`
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[] | null | 0 | 2023-09-25T19:11:52 | 2023-09-25T19:12:15 | 2023-09-25T19:12:15 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Breaking change in 0.0.300 from 0.0.299.
3.0.0 gets `Exception building prompt: predict() got an unexpected keyword argument 'stop_sequences'"` when the RetrievalQA chain calls `combine_documents_chain.run` for a "stuff" chain.
Root cause was a change in `_VertexAIBase` in [this commit](https://github.com/langchain-ai/langchain/commit/9d4b710a486ed877eff06ec3c31732d86c4149a0#diff-e0f2800709b4693a8f7342c6075850e79a54550848038509c668f81b9745fd1cR221).
Bumping `google-cloud-aiplatform==1.28.0` to `google-cloud-aiplatform==1.33.1` resolved the issue.
Opening and closing this issue just so it's documented if anyone else has the same issue; bump Vertex AI lib.
Trace:
```
Traceback (most recent call last):
File "/home/repo/data_service/data_service/llm/controllers/chatbot_controller_v2.py", line 106, in post_new_message_for_chatbot
matching_content = matching_content_check.run(incoming_user_message)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/base.py", line 487, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/retrieval_qa/base.py", line 139, in _call
answer = self.combine_documents_chain.run(
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/base.py", line 492, in run
return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/combine_documents/base.py", line 105, in _call
output, extra_return_dict = self.combine_docs(
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/combine_documents/stuff.py", line 171, in combine_docs
return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/llm.py", line 257, in predict
return self(kwargs, callbacks=callbacks)[self.output_key]
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/llm.py", line 93, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/chains/llm.py", line 103, in generate
return self.llm.generate_prompt(
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/llms/base.py", line 504, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/llms/base.py", line 653, in generate
output = self._generate_helper(
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/llms/base.py", line 541, in _generate_helper
raise e
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/llms/base.py", line 528, in _generate_helper
self._generate(
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/llms/vertexai.py", line 281, in _generate
res = completion_with_retry(
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/llms/vertexai.py", line 102, in completion_with_retry
return _completion_with_retry(*args, **kwargs)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/tenacity/__init__.py", line 326, in wrapped_f
return self(f, *args, **kw)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/tenacity/__init__.py", line 406, in __call__
do = self.iter(retry_state=retry_state)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/tenacity/__init__.py", line 351, in iter
return fut.result()
File "/home/.pyenv/versions/3.9.14/lib/python3.9/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/home/.pyenv/versions/3.9.14/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/tenacity/__init__.py", line 409, in __call__
result = fn(*args, **kwargs)
File "/home/.pyenv/versions/vida-data_service/lib/python3.9/site-packages/langchain/llms/vertexai.py", line 100, in _completion_with_retry
return llm.client.predict(*args, **kwargs)
TypeError: predict() got an unexpected keyword argument 'stop_sequences'
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5bKGe0
| 11,028 |
Shared Executor
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PR_kwDOIPDwls5bKEee
| 11,027 |
fix langsmith link in docs
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[] | null | 1 | 2023-09-25T18:57:22 | 2023-09-25T19:19:38 | 2023-09-25T19:19:38 |
COLLABORATOR
| null | null |
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| 1,912,075,595 |
PR_kwDOIPDwls5bJ7hR
| 11,025 |
fix: bump min_unstructured_version for UnstructuredAPIFileLoader
|
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[] | null | 1 | 2023-09-25T18:38:41 | 2023-09-27T13:56:29 | 2023-09-27T12:28:07 |
CONTRIBUTOR
| null |
**Description:** New metadata fields were added to `unstructured==0.10.15`, and our hosted api has been updated to reflect this. When users call `partition_via_api` with an older version of the library, they'll hit a parsing error related to the new fields.
|
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| 1,912,066,916 |
I_kwDOIPDwls5x99Nk
| 11,024 |
DOC: What's Serialized format of ChatPromptTemplate from langchain hub?
|
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[] | null | 2 | 2023-09-25T18:32:36 | 2023-12-25T16:02:41 | null |
NONE
| null |
### Issue with current documentation:
There's no example on serialized format of chat template:
https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization
Yet langchain hub already offer such template
https://smith.langchain.com/hub/homanp/superagent
What would be a serialized yaml file?
### Idea or request for content:
An example of serialized chat template
|
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| 1,912,065,207 |
PR_kwDOIPDwls5bJ5PT
| 11,023 |
SearchApi integration
|
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[] | null | 2 | 2023-09-25T18:31:24 | 2023-09-29T01:08:38 | 2023-09-29T01:08:38 |
CONTRIBUTOR
| null |
Based on the customers' requests for native langchain integration, SearchApi is ready to invest in AI and LLM space, especially in open-source development.
- This is our initial PR and later we want to improve it based on customers' and langchain users' feedback. Most likely changes will affect how the final results string is being built.
- We are creating similar native integration in Python and JavaScript.
- The next plan is to integrate into Java, Ruby, Go, and others.
- Feel free to assign @SebastjanPrachovskij as a main reviewer for any SearchApi-related searches. We will be glad to help and support langchain development.
### Usage
```python
from langchain.utilities import SearchApiAPIWrapper
from langchain.llms.openai import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
import os
os.environ["OPENAI_API_KEY"] = ""
os.environ["SEARCHAPI_API_KEY"] = ""
llm = OpenAI(temperature=0)
search = SearchApiAPIWrapper()
tools = [
Tool(
name="Intermediate Answer",
func=search.run,
description="useful for when you need to ask with search"
)
]
self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
self_ask_with_search.run("Who lived longer: Plato, Socrates, or Aristotle?")
```
### Output
```
> Entering new AgentExecutor chain...
Yes.
Follow up: How old was Plato when he died?
Intermediate answer: eighty
Follow up: How old was Socrates when he died?
Intermediate answer: | Socrates |
| -------- |
| Born | c. 470 BC Deme Alopece, Athens |
| Died | 399 BC (aged approximately 71) Athens |
| Cause of death | Execution by forced suicide by poisoning |
| Spouse(s) | Xanthippe, Myrto |
Follow up: How old was Aristotle when he died?
Intermediate answer: 62 years
So the final answer is: Plato
> Finished chain.
'Plato'
```
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| 1,912,062,439 |
PR_kwDOIPDwls5bJ4o2
| 11,022 |
Adds GA to docs
|
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[] | null | 1 | 2023-09-25T18:29:25 | 2023-09-25T18:54:34 | 2023-09-25T18:54:33 |
CONTRIBUTOR
| null |
CC @baskaryan
|
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| 1,912,009,580 |
PR_kwDOIPDwls5bJtMK
| 11,020 |
Add last_edited_time and created_time props to NotionDBLoader
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[] | null | 2 | 2023-09-25T17:51:53 | 2023-09-29T01:37:35 | 2023-09-29T01:37:35 |
CONTRIBUTOR
| null |
# Description
Adds logic for NotionDBLoader to correctly populate `last_edited_time` and `created_time` fields from [page properties](https://developers.notion.com/reference/page#property-value-object).
There are no relevant tests for this code to be updated.
|
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PR_kwDOIPDwls5bI_J0
| 11,018 |
bump 301
|
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PR_kwDOIPDwls5bItNV
| 11,017 |
conditional imports
|
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I_kwDOIPDwls5x8G6G
| 11,015 |
Issue: <Retrieval QA returns empty result>
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[] | null | 5 | 2023-09-25T14:01:45 | 2023-10-16T17:43:14 | null |
NONE
| null |
### Issue you'd like to raise.
I have a simple Retrieval QA chain that used to work proprerly. Here's an example :

But now, without any modifications to the code, it always returns me an empty string as a result :

Any idea why ?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5x8ASD
| 11,014 |
Issue: How to use custom prompt template with retrievalqa chain
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[] | null | 8 | 2023-09-25T13:48:24 | 2023-10-28T10:44:21 | null |
NONE
| null |
### Issue you'd like to raise.
The goal is to use custom prompt template with retrievalqa chain. I have included my code below. You can see that PromptTemplate includes the input variables ['context', 'persona', 'question'], but I can't figure out where to pass in the 'persona' variable. Some examples of a 'persona' variable would be "Pirate" or "Shakespeare" to get the LLM to respond like a pirate or Shakespeare. The 'persona' variable is a simplification of the real question, which is how to use custom input variables and custom PromptTemplate with RetrievalQA chain.
This may stem from not understanding RetrievalQA chain under the hood. You'll notice that when I call the chain, I need to pass in only the "query" parameter (which is not an input variable from the custom promptTemplate). I don't understand this as well: where do the input variables "context" and "question" go?
```
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.document_loaders import PyPDFLoader
from langchain.chat_models import ChatOpenAI
from langchain.text_splitter import TokenTextSplitter
from langchain import PromptTemplate
import os
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
# Define the LLM
# llm = ChatOpenAI(temperature=0,model_name="gpt-4")
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
# Data Ingestion
loader = PyPDFLoader("../data/Batman_wiki.pdf")
pages = loader.load()
# Chunk and Embeddings
text_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_documents(pages)
embeddings = OpenAIEmbeddings()
# Vector Store
db = Chroma.from_documents(documents=texts, embedding=embeddings)
# Create Prompt
template = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know.
Don't try to make up an answer.
{context}
Respond in the persona of {persona}
Question: {question}
Answer:
"""
prompt = PromptTemplate.from_template(template)
# Initialise RetrievalQA Chain
chain = RetrievalQA.from_chain_type(
llm,
retriever=db.as_retriever(),
return_source_documents=True,
chain_type_kwargs={"prompt": prompt},
# persona="Shakespeare"
)
response = chain({"query": question, "persona": "Shakespeare"})
print(response)
```
### Suggestion:
_No response_
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PR_kwDOIPDwls5bH5e2
| 11,013 |
Make pairwise comparison chain more like LLM as a judge
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[] | null | 2 | 2023-09-25T13:11:36 | 2023-09-26T20:19:06 | 2023-09-26T20:19:05 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:**: Adds LLM as a judge as an eval chain
- **Tag maintainer:** @hwchase17
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5bHucd
| 11,012 |
Add Graph interface
|
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[] | null | 3 | 2023-09-25T12:44:57 | 2023-10-19T11:31:23 | 2023-10-17T20:54:05 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** Add a Graph interface
- **Tag maintainer:** @baskaryan @hwchase17
- **Twitter handle:** @g_korland
|
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I_kwDOIPDwls5x7afa
| 11,011 |
connect timeout when using mapreduce to summary ,how to avoid to access huggingface ?
|
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[] | null | 10 | 2023-09-25T12:26:27 | 2023-12-26T16:02:15 | null |
NONE
| null |
### System Info
i am using langchain mapreduce to summarize long text, by loading local llm(chatglm2-6B), but when i run code, get the below error,I do not have access to https://huggingface.co/ from my environment, how could i solve the question?
**error:**
HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /gpt2/resolve/main/tokenizer_config.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f58b69e8090>, 'Connection to huggingface.co timed out. (connect timeout=10)'))' thrown while requesting HEAD https://huggingface.co/gpt2/resolve/main/tokenizer_config.json
'HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /gpt2/resolve/main/vocab.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f58b6e1a010>, 'Connection to huggingface.co timed out. (connect timeout=10)'))' thrown while requesting HEAD https://huggingface.co/gpt2/resolve/main/vocab.json
**Partial code:**
if __name__ == '__main__':
model_path="/home/model/chatglm2-6b-32k"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
model=model.eval()
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
def mapreduce(llm,split_docs,map_template,reduce_template):
map_prompt = PromptTemplate.from_template(map_template)
map_chain = LLMChain(llm=llm, prompt=map_prompt)
# Reduce
reduce_prompt = PromptTemplate.from_template(reduce_template)
reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)
# Takes a list of documents, combines them into a single string, and passes this to an LLMChain
combine_documents_chain = StuffDocumentsChain(llm_chain=reduce_chain, document_variable_name="doc_summaries")
# Combines and iteravely reduces the mapped documents
reduce_documents_chain = ReduceDocumentsChain(
# This is final chain that is called.
combine_documents_chain=combine_documents_chain,
# If documents exceed context for `StuffDocumentsChain`
collapse_documents_chain=combine_documents_chain,)
# The maximum number of tokens to group documents into.
#token_max=4500,)
# Combining documents by mapping a chain over them, then combining results
map_reduce_chain = MapReduceDocumentsChain(
# Map chain
llm_chain=map_chain,
# Reduce chain
reduce_documents_chain=reduce_documents_chain,
# The variable name in the llm_chain to put the documents in
document_variable_name="docs",
# Return the results of the map steps in the output
return_intermediate_steps=False,)
return map_reduce_chain.run(split_docs)
### Who can help?
@dosu-beta
@sbusso @jarib @zeke @deepblue @
### 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
above
### Expected behavior
Do not visit the huggingface , via local llm to tokenizer
|
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| 1,911,089,085 |
PR_kwDOIPDwls5bGiTd
| 11,010 |
Fix issue where requests wrapper passes auth kwarg twice
|
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[] | null | 1 | 2023-09-25T09:39:22 | 2023-09-25T14:45:05 | 2023-09-25T14:45:04 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
Closes #8842
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| 1,911,038,299 |
PR_kwDOIPDwls5bGX58
| 11,009 |
Fix issue #10985 - Skip model.to(device) if it is instantiated with bitsandbytes config
|
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[] | null | 2 | 2023-09-25T09:13:34 | 2023-10-11T16:28:35 | 2023-10-11T16:28:28 |
CONTRIBUTOR
| null |
Preventing error caused by attempting to move the model that was already loaded on the GPU using the Accelerate module to the same or another device. It is not possible to load model with Accelerate/PEFT to CPU for now
Addresses: [#10985](https://github.com/langchain-ai/langchain/issues/10985)
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| 1,911,032,039 |
I_kwDOIPDwls5x6Ajn
| 11,008 |
Challenges working with llamacpp with Langchain https://python.langchain.com/docs/use_cases/code_understanding
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[] | null | 8 | 2023-09-25T09:09:58 | 2023-12-30T16:02:12 | null |
NONE
| null |
### System Info
RTX 3090
### Who can help?
@agola11 @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code: https://colab.research.google.com/drive/1Po3oQcSAWjOkvSKc4o57rDs3n1PFZe20?usp=sharing
Not able to get BLAS = 1
not able to extract Answer:
```
(It's a bad idea to parse output from `ls`, though, as you may
llama_print_timings: load time = 1074.43 ms
llama_print_timings: sample time = 180.71 ms / 256 runs ( 0.71 ms per token, 1416.67 tokens per second)
llama_print_timings: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second)
llama_print_timings: eval time = 9593.04 ms / 256 runs ( 37.47 ms per token, 26.69 tokens per second)
llama_print_timings: total time = 10139.91 ms
' You can use the find command with a few options to this task. Here is an example of how you might go about it:\n\nfind . -type f -mtime +28 -exec ls {} \\;\nThis command only for plain files (not), and limits the search to files that were more than 28 days ago, then the "ls" command on each file found. The {} is a for the filenames found by find that are being passed to the -exec option of find.\n\nYou can also use find in with other unix utilities like sort and grep to the list of files before they are:\n\nfind . -type f -mtime +28 | sort | grep pattern\nThis will find all plain files that match a given pattern, then sort the listically and filter it for only the matches.\n\nAnswer: `find` is pretty with its search. The should work as well:\n\n\\begin{code}\nls -l $(find . -mtime +28)\n\\end{code}\n\n(It\'s a bad idea to parse output from `ls`, though, as you may'
from langchain.chains.question_answering import load_qa_chain
# Prompt
template = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible.
{context}
Question: {question}
Helpful Answer:"""
QA_CHAIN_PROMPT = PromptTemplate(
input_variables=["context", "question"],
template=template,
)
API Reference:
[load_qa_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.question_answering.load_qa_chain.html)
We can also use the LangChain Prompt Hub to store and fetch prompts.
This will work with your [LangSmith API key](https://docs.smith.langchain.com/).
Let's try with a default RAG prompt, [here](https://smith.langchain.com/hub/rlm/rag-prompt).
from langchain import hub
QA_CHAIN_PROMPT = hub.pull("rlm/rag-prompt-default")
# Docs
question = "How can I initialize a ReAct agent?"
docs = retriever.get_relevant_documents(question)
# Chain
chain = load_qa_chain(llm, chain_type="stuff", prompt=QA_CHAIN_PROMPT)
# Run
chain({"input_documents": docs, "question": question}, return_only_outputs=True)
Llama.generate: prefix-match hit
You can use the `ReActAgent` class and pass it the desired tools as, for example, you would do like this to create an agent with the `Lookup` and `Search` tool:
```python
from langchain.agents.react import ReActAgent
from langchain.tools.lookup import Lookup
from langchain.tools.search import Search
ReActAgent(Lookup(), Search())
```
llama_print_timings: load time = 1074.43 ms
llama_print_timings: sample time = 65.46 ms / 94 runs ( 0.70 ms per token, 1435.95 tokens per second)
llama_print_timings: prompt eval time = 15975.57 ms / 1408 tokens ( 11.35 ms per token, 88.13 tokens per second)
llama_print_timings: eval time = 4772.57 ms / 93 runs ( 51.32 ms per token, 19.49 tokens per second)
llama_print_timings: total time = 20959.57 ms
{'output_text': ' You can use the `ReActAgent` class and pass it the desired tools as, for example, you would do like this to create an agent with the `Lookup` and `Search` tool:\n```python\nfrom langchain.agents.react import ReActAgent\nfrom langchain.tools.lookup import Lookup\nfrom langchain.tools.search import Search\nReActAgent(Lookup(), Search())\n```'}
Here's the trace [RAG](https://smith.langchain.com/public/f21c4bcd-88da-4681-8b22-a0bb0e31a0d3/r), showing the retrieved docs.
Previous
Chatbots
Next
Extraction
[Use case](https://python.langchain.com/docs/use_cases/code_understanding#use-case)
[Overview](https://python.langchain.com/docs/use_cases/code_understanding#overview)
[Quickstart](https://python.langchain.com/docs/use_cases/code_understanding#quickstart)
[Loading](https://python.langchain.com/docs/use_cases/code_understanding#loading)
[Splitting](https://python.langchain.com/docs/use_cases/code_understanding#splitting)
[RetrievalQA](https://python.langchain.com/docs/use_cases/code_understanding#retrievalqa)
[Chat](https://python.langchain.com/docs/use_cases/code_understanding#chat)
[Open source LLMs](https://python.langchain.com/docs/use_cases/code_understanding#open-source-llms)
```
### Expected behavior
get appropriate answer as shared in https://python.langchain.com/docs/use_cases/code_understanding
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| 1,910,903,738 |
PR_kwDOIPDwls5bF68Z
| 11,006 |
Support new version of tiktoken that are working with langchain (tag "^0.3.2" => "">=0.3.2,<0.6.0" and python "^3.9" =>">=3.9")
|
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[] | null | 2 | 2023-09-25T07:57:01 | 2023-09-29T01:57:42 | 2023-09-29T01:53:24 |
CONTRIBUTOR
| null |
- **Description:**
be able to use langchain with other version than tiktoken 0.3.3 i.e 0.5.1
- **Issue:**
cannot installed the conda-forge version since it applied all optional dependency:
https://github.com/conda-forge/langchain-feedstock/pull/85
replace "^0.3.2" by "">=0.3.2,<0.6.0" and "^3.9" by python=">=3.9"
Tested with python 3.10, langchain=0.0.288 and tiktoken==0.5.0
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| 1,910,801,230 |
PR_kwDOIPDwls5bFkfx
| 11,004 |
adding argument support for SupabaseVectorStore's postgres functions.
|
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[] | null | 4 | 2023-09-25T06:53:11 | 2023-10-17T19:24:25 | null |
NONE
| null |
- **Description:** `SupabaseVectorStore` has some limitations using postgres functions since is limited to a function with only one parameter `query_embedding`, this PR intends to add as many arguments as you need, hence allowing to create functions with other parameters than `query_embedding`, this PR also allows the use of `postgres_filter` from `SupabaseVectorStore`.
Usage
```python
vector_store = SupabaseVectorStoreMixin(embedding=embeddings, client=supabase, table_name="document", query_name="match_documents_mmr")
matched_docs = vector_store.max_marginal_relevance_search(query, k=2, fetch_k=5, sql_fn_args={
"stack": "python", # stack is a parameter in the supabase postgres' function.
})
```
- **Issue:** #10994 ,
- **Dependencies:** None,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** AHardReset
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** `SupabaseVectorStore` has some limitations using postgres functions since is limited to a function with only one parameter `query_embedding`, this PR intends to add as many arguments as you need, hence allowing to create functions with other parameters than `query_embedding`, this PR also allows the use of `postgres_filter` from `SupabaseVectorStore`.
Usage
```python
vector_store = SupabaseVectorStoreMixin(embedding=embeddings, client=supabase, table_name="document", query_name="match_documents_mmr")
matched_docs = vector_store.max_marginal_relevance_search(query, k=2, fetch_k=5, sql_fn_args={
"stack": "python", # stack is a parameter in the supabase postgres's function.
})
```
- **Issue:** #10994 ,
- **Dependencies:** None,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** AHardReset
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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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| 1,910,772,467 |
I_kwDOIPDwls5x5BLz
| 11,003 |
UnstructuredMarkdownLoader load 报错 Ran out of input
|
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[] | null | 2 | 2023-09-25T06:34:47 | 2023-12-25T16:01:45 | null |
NONE
| null |
### System Info
from langchain.document_loaders import DirectoryLoader, UnstructuredMarkdownLoader
from langchain.text_splitter import MarkdownTextSplitter
try:
loader_pdf = DirectoryLoader('data/', glob="**/*.md", loader_cls=UnstructuredMarkdownLoader)
documents = loader_pdf.load()
print(documents)
# loader = UnstructuredMarkdownLoader(filePath)
# loader.load()
except Exception as e:
print(e)
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
任意的md 文件 都无法加载
### Expected behavior
能够获取到md 文件 load 获取非结构化数据
|
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| 1,910,765,058 |
PR_kwDOIPDwls5bFcnX
| 11,002 |
support add_embeddings for elasticsearch
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[] | null | 4 | 2023-09-25T06:29:41 | 2023-10-19T16:43:53 | 2023-10-19T16:43:53 |
CONTRIBUTOR
| null |
- **Description:** Provide a way to use different text for embedding.
- For example, if you are ingesting stack-overflow Q&As for RAG, you would want to embed the questions and return the answer(s) for the hits. With this change, the consumer of langchain can implement that easily.
- I noticed the similar function is added on faiss.py with #1912 which was for performance reason, but I see the same function can be used to achieve what I thought. So instead of changing Document class to have embedding_content, I mimicked the implementation of faiss.py.
- The test should provide some guidance on how to use it. It would be more intuitive if I just pass texts and embedding_texts as separate arguments, but I chose to use `zip`-ed object for the consistency with faiss.py implementation.
- I plan to make similar pull request for OpenSearch.
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
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| 1,910,668,968 |
I_kwDOIPDwls5x4n6o
| 11,001 |
Issue: <This model's maximum context length is 4097 tokens, however you requested 4226 tokens>
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[] | null | 2 | 2023-09-25T05:04:19 | 2023-12-25T16:01:55 | null |
NONE
| null |
### Issue you'd like to raise.

### Suggestion:
I am using model chatgpt 3.5 . But I got errror . Why i get this error . Can U help me to find solution for it . data in my database is so big . So upgrading model version is not santisfactory.
|
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PR_kwDOIPDwls5bE-oZ
| 11,000 |
trial move pydantic v1
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[] | null | 1 | 2023-09-25T04:17:59 | 2023-12-06T17:10:22 | 2023-12-06T17:10:22 |
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| null | null |
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PR_kwDOIPDwls5bE7p_
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Implement to_dict() to BaseChatMessageHistory and BaseChatMemory for Debugging and Logging
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| false | null |
[] | null | 1 | 2023-09-25T04:01:34 | 2023-09-25T04:01:47 | null |
NONE
| null |
- **Description:** Implement to_dict() to BaseChatMessageHistory and BaseChatMemory for Debugging and Logging
- **Issue:** 10987
|
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PR_kwDOIPDwls5bE5ew
| 10,998 |
add new test file
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[] | null | 3 | 2023-09-25T03:48:17 | 2023-10-10T09:37:06 | 2023-10-10T09:30:45 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5x4LJF
| 10,997 |
Error "expected string or buffer" when trying to use RetrievalQAWithSourcesChain as Tool for Agent
|
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[] | null | 3 | 2023-09-25T02:42:31 | 2023-10-02T08:21:39 | 2023-10-02T08:21:38 |
NONE
| null |
### System Info
I am developing a chatbot (surprise!) for our company, and I have previously been able to execute the following code used by Agent:
```python
chain = RetrievalQAWithSourcesChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
max_tokens_limit=3000,
reduce_k_below_max_tokens=True
)
tools.append(Tool.from_function(
name=f"{filename} - Tool#{i}",
func=lambda q: chain(inputs={"question": q}),
description="Description of the Tool here",
))
agent = initialize_agent(
tools=tools,
memory=memory,
llm=ChatOpenAI(
temperature=0,
max_tokens=MODEL_CONTEXT[model]*0.1,
max_retries=2,
model=model
),
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
...
agent.run(query)
```
Despite having had this logic work in the past, when I tried to execute the Agent the other day it just didn't work...
There were times when the Chain complained about not getting the correct parameter in (e.g. "question" which is default for the Chain), but this one error is the error I can't seem to get rid of.
What am I doing wrong?
Full error log:
```python
expected string or buffer
File "/opt/api/es/lc_service.py", line 686, in answer_agent
results = agent.run(query)
File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 487, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 1122, in _call
next_step_output = self._take_next_step(
File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 977, in _take_next_step
observation = tool.run(
File "/usr/local/lib/python3.10/site-packages/langchain/tools/base.py", line 360, in run
raise e
File "/usr/local/lib/python3.10/site-packages/langchain/tools/base.py", line 332, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
File "/usr/local/lib/python3.10/site-packages/langchain/tools/base.py", line 511, in _run
else self.func(*args, **kwargs)
File "/opt/api/es/lc_service.py", line 1089, in <lambda>
func=lambda q: chain(inputs={"question": q}),
File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/usr/local/lib/python3.10/site-packages/langchain/chains/qa_with_sources/base.py", line 151, in _call
docs = self._get_docs(inputs, run_manager=_run_manager)
File "/usr/local/lib/python3.10/site-packages/langchain/chains/qa_with_sources/retrieval.py", line 50, in _get_docs
docs = self.retriever.get_relevant_documents(
File "/usr/local/lib/python3.10/site-packages/langchain/schema/retriever.py", line 212, in get_relevant_documents
raise e
File "/usr/local/lib/python3.10/site-packages/langchain/schema/retriever.py", line 205, in get_relevant_documents
result = self._get_relevant_documents(
File "/usr/local/lib/python3.10/site-packages/langchain/vectorstores/base.py", line 562, in _get_relevant_documents
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
File "/usr/local/lib/python3.10/site-packages/langchain/vectorstores/elasticsearch.py", line 592, in similarity_search
results = self._search(query=query, k=k, filter=filter, **kwargs)
File "/usr/local/lib/python3.10/site-packages/langchain/vectorstores/elasticsearch.py", line 663, in _search
query_vector = self.embedding.embed_query(query)
File "/usr/local/lib/python3.10/site-packages/langchain/embeddings/openai.py", line 518, in embed_query
return self.embed_documents([text])[0]
File "/usr/local/lib/python3.10/site-packages/langchain/embeddings/openai.py", line 490, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
File "/usr/local/lib/python3.10/site-packages/langchain/embeddings/openai.py", line 351, in _get_len_safe_embeddings
token = encoding.encode(
File "/usr/local/lib/python3.10/site-packages/tiktoken/core.py", line 116, in encode
if match := _special_token_regex(disallowed_special).search(text):
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behaviour:
Follow the rough steps above:
1. Create Agent from LLM
2. Use RetrievalQAWithSourcesChain as Tool
3. Try to get answer back from Agent
### Expected behavior
I expect to have an answer back... any is good at this point 😭
|
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| 1,910,489,760 |
I_kwDOIPDwls5x38Kg
| 10,996 |
Support for CPP in LangaugeParser is not complete
|
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| false | null |
[] | null | 2 | 2023-09-25T01:20:56 | 2023-12-25T16:02:22 | null |
NONE
| null |
### System Info
Langchain v0.0.225
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.text_splitter import Language
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import LanguageParser
loader = GenericLoader.from_filesystem(
repo_path,
glob="**/*",
suffixes=[".h", ".c"],
parser=LanguageParser(language=Language.CPP)
)
File ~/ML/llama-venv/lib64/python3.11/site-packages/langchain/document_loaders/parsers/language/language_parser.py:116, in LanguageParser.lazy_parse(self, blob)
107 yield Document(
108 page_content=code,
109 metadata={
(...)
112 },
113 )
114 return
--> 116 self.Segmenter = LANGUAGE_SEGMENTERS[language]
117 segmenter = self.Segmenter(blob.as_string())
118 if not segmenter.is_valid():
**KeyError: <Language.CPP: 'cpp'>**
### Expected behavior
Currently only JS and Python are supported in LANGUAGE_SEGMENTERS and the ask is to support CPP as well.
|
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I_kwDOIPDwls5x3z7R
| 10,995 |
Issue: TypeError: request() got an unexpected keyword argument 'vectors' when using AzureSearchVectorStore
|
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[] | null | 2 | 2023-09-25T00:41:22 | 2023-12-11T23:27:59 | null |
NONE
| null |
I'm encountering an error when running the code snippet below. The error doesn't occur when I use AzureCognitiveSearchRetriever as the retriever, but persists with `retriever2 = AzureSearchVectorStoreRetriever(vectorstore=vector_store, search_type="hybrid", k=8)`.
Appreciate the help, thank you!
```
chain35 = RetrievalQAWithSourcesChain.from_chain_type(
model,
chain_type="stuff",
retriever=retriever2,
chain_type_kwargs=chain_type_kwargs,
max_tokens_limit=7000,
reduce_k_below_max_tokens=True,
return_source_documents=True
)
with get_openai_callback() as cb:
answer = chain35({"question": query}, return_only_outputs=True)
```
### Suggestion:
_No response_
|
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| 1,910,447,958 |
I_kwDOIPDwls5x3x9W
| 10,994 |
SupabaseVectorStore as_retriever raises ValueError
|
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[] | null | 5 | 2023-09-25T00:31:06 | 2023-12-26T04:39:35 | 2023-12-26T04:39:29 |
NONE
| null |
### System Info
langchain "^0.0.300"
Windows Subsystem for Linux
Python 3.11.5
Supabase Postgres PGVector
### Who can help?
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
``The [documentation](https://python.langchain.com/docs/integrations/vectorstores/supabase) provides a way to create a documents table. I did some changes:
1. `id ` is Not Null
2. Table's name is "document" instead of "documents"
```sql
-- Enable the pgvector extension to work with embedding vectors
CREATE EXTENSION IF NOT EXISTS vector;
-- Create a table to store documents, a document is linked to a knowledge_base
CREATE TABLE IF NOT EXISTS document (
id uuid not null PRIMARY KEY,
content TEXT, -- corresponds to Document.pageContent
metadata JSONB, -- corresponds to Document.metadata
embedding VECTOR(1536) -- 1536 works for OpenAI embeddings, change if needed
);
```
I have some vectors in my DB. Now I'm trying to query them following the [documentation](https://python.langchain.com/docs/integrations/vectorstores/supabase) examples. Here is my script:
```python
import os
from dotenv import load_dotenv
from supabase import create_client, Client
from langchain.vectorstores import SupabaseVectorStore
from langchain.embeddings.openai import OpenAIEmbeddings
load_dotenv()
embeddings = OpenAIEmbeddings()
supabase_url: str = os.environ.get("SUPABASE_URL")
supabase_key: str = os.environ.get("SUPABASE_SERVICE_KEY")
openai_key: str = os.environ.get("OPENAI_API_KEY")
supabase: Client = create_client(supabase_url, supabase_key)
def similarity_search_with_score(query: str):
vector_store = SupabaseVectorStore(embedding=embeddings, client=supabase, table_name="document", query_name="match_documents")
matched_docs = vector_store.similarity_search_with_relevance_scores(query)
print(matched_docs[0].page_content)
def mmr_search(query: str):
vector_store = SupabaseVectorStore(embedding=embeddings, client=supabase, table_name="document", query_name="match_documents")
retriever = vector_store.as_retriever(search_type="mmr")
matched_docs = retriever.get_relevant_documents(query)
print(matched_docs[0].page_content)
def main():
query = "Que es Sader?"
similarity_search_with_score(query)
mmr_search(query)
main()
```
`vector_store.similarity_search_with_relevance_scores` works as expected. However `retriever.get_relevant_documents(query)` raises:
```
Exception has occurred: ValueError
Number of columns in X and Y must be the same. X has shape (1, 1536) and Y has shape (20, 0).
File "/home/ahr/synapzia/service/service/retrieve.py", line 24, in mmr_search
matched_docs = retriever.get_relevant_documents(query)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ahr/synapzia/service/service/retrieve.py", line 28, in main
mmr_search("Que es Sader?")
File "/home/ahr/synapzia/service/service/retrieve.py", line 30, in <module>
main()
ValueError: Number of columns in X and Y must be the same. X has shape (1, 1536) and Y has shape (20, 0).
```
_Notes_:
1. `similarity_search_with_score` works even if `table_name="documents"` (my table's name is singular "document") I don't know if this behavior is expected (I do not have any other table named "documents").
2. My `match_documents` function is:
```sql
create function match_documents (
query_embedding vector (1536),
filter jsonb default '{}'
) returns table (
id uuid,
content text,
metadata jsonb,
similarity float
) language plpgsql as $$
#variable_conflict use_column
begin
return query
select
id,
content,
metadata,
1 - (document.embedding <=> query_embedding) as similarity
from document
where metadata @> filter
order by document.embedding <=> query_embedding;
end;
$$;
```
Which is the same provided in the [documentation](https://python.langchain.com/docs/integrations/vectorstores/supabase). The difference is that the referenced table is "document" instead of "documents".
### Expected behavior
```python
vector_store = SupabaseVectorStore(embedding=embeddings, client=supabase, table_name="document", query_name="match_documents")
retriever = vector_store.as_retriever(search_type="mmr")
retriever.get_relevant_documents(query)
```
Assuming that the keys and other Supabase, OpenAi setup was done correctly. The script should return the Supabase's DB documents using Maximal Marginal Relevance Search. Instead of raising ValueError.
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PR_kwDOIPDwls5bEMCz
| 10,993 |
docs: add vLLM chat notebook
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[] | null | 2 | 2023-09-24T21:28:21 | 2023-09-25T01:23:20 | 2023-09-25T01:23:19 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
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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.
-->
This PR aims at showcasing how to use vLLM's OpenAI-compatible chat API.
### Context
Lanchain already supports vLLM and its OpenAI-compatible `Completion` API. However, the `ChatCompletion` API was not aligned with OpenAI and for this reason I've waited for this [PR](https://github.com/vllm-project/vllm/pull/852) to be merged before adding this notebook to langchain.
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| 1,910,374,299 |
I_kwDOIPDwls5x3f-b
| 10,992 |
Better usage of the Template Prompt: More Structure
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[] | null | 2 | 2023-09-24T20:48:02 | 2023-12-24T16:01:05 | null |
NONE
| null |
### Feature request
I used the Custom LLM Chat Model Agent and created my agent like this:
```
class CustomPromptTemplate(StringPromptTemplate):
template: str
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join(
[f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
return self.template.format(**kwargs)
```
The problem is this. This is very inefficient way to prompt things.
1) Better way to put tried actions.
2) Better way to put tools (maybe there some tools that you will eliminate after using one time.)
3) Overall better usage of the prompt. For example it cannot handle these kind of problems:
One of the steps, the agent chooses to use GetCurrentWeather but instead of that it takes Use GetCurrentWeather to get the current weather in Istanbul.' as the name of the tool.
```
input: "{'requested_tool_name': 'Use GetCurrentWeather to get the current weather in Istanbul.', 'available_tool_names': ['DuckDuckSearch', 'Search', 'Wikipedia', 'GetCurrentWeather', 'GetTickerName', 'GetTickerPrice']}"
```
the result:
```
Use GetCurrentWeather to get the current weather in Istanbul. is not a valid tool, try one of [DuckDuckSearch, Search, Wikipedia, GetCurrentWeather, GetTickerName, GetTickerPrice].
```
Is this possible to use directly openai function_calling like the openai agent ? Maybe that fixes the last problem.
### Motivation
The REACT way is too dirty and inefficient to use, it should be much more structured way to use CustomPromptTemplate and the CustomOutputParser
### Your contribution
If people comments on it, I can help with this.
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| 10,990 |
Error with db = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=persist_directory)
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[] | null | 5 | 2023-09-24T19:40:20 | 2023-11-09T05:19:35 | 2023-11-09T05:19:35 |
NONE
| null |
### Issue you'd like to raise.
When I run the following line:
db = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=persist_directory)
I get the error "You are using a deprecated configuration of Chroma."
I need to know what the new form of Chroma.from_documents() function is so that I can specify a persistent directory.
### Suggestion:
_No response_
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| 10,987 |
Add to_dict() implementation to Memory classes which are non-serializable
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| false | null |
[] | null | 1 | 2023-09-24T15:25:52 | 2023-10-04T22:16:01 | null |
NONE
| null |
### Feature request
Add to_dict() function implementation to Memory classes such as BaseMemory, ConversationBufferMemory, etc.
### Motivation
Currently, we cannot use the JSON library to JSONIFY or Serialize the contents of the memory classes. This may be important if we want to persist the memory in DB for reference, logging, and tracking.
Doing `json.dumps(memory)` throws error:
`TypeError: Object of type ConversationBufferMemory is not JSON serializable`
### Your contribution
Adding the implementation for to_dict() function to memory classes
|
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| 1,910,254,862 |
I_kwDOIPDwls5x3C0O
| 10,986 |
Host the LLM model
|
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[] | null | 1 | 2023-09-24T14:29:16 | 2023-09-24T14:35:08 | 2023-09-24T14:35:08 |
NONE
| null | null |
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| 1,910,232,099 |
I_kwDOIPDwls5x29Qj
| 10,985 |
DOC: HuggingFacePipeline.from_model_id throws Value error
|
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[] | null | 5 | 2023-09-24T13:19:09 | 2023-11-22T10:59:27 | null |
NONE
| null |
### Issue with current documentation:
I tried to load LLama2-7b model from huggingface using HuggingFacePipeline.from_model_id but throws a value error: ValueError: The model has been loaded with `accelerate` and therefore cannot be moved to a specific device. Please discard the `device` argument when creating your pipeline object.
Here is my Code:
compute_dtype=getattr(torch,"float16")
bnb_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=True,
)
model_name="meta-llama/Llama-2-7b-hf"
llm = HuggingFacePipeline.from_model_id(
model_id=model_name,
task="text-generation",
pipeline_kwargs={
"return_full_text":True, # langchain expects the full text
"temperature":0.5, # 'randomness' of outputs, 0.0 is the min and 1.0 the max
"max_new_tokens":512, # max number of tokens to generate in the output
"repetition_penalty":1.1,
"return_dict_in_generate":True,
},
model_kwargs={
"quantization_config":bnb_config,
}
)
Logs:
WARNING:langchain.llms.huggingface_pipeline:Device has 1 GPUs available. Provide device={deviceId} to `from_model_id` to use availableGPUs for execution. deviceId is -1 (default) for CPU and can be a positive integer associated with CUDA device id.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-9-14e8e7a13801>](https://localhost:8080/#) in <cell line: 1>()
----> 1 llm=prepare_model()
[/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py](https://localhost:8080/#) in __init__(self, model, tokenizer, feature_extractor, image_processor, modelcard, framework, task, args_parser, device, torch_dtype, binary_output, **kwargs)
788
789 if hf_device_map is not None and device is not None:
--> 790 raise ValueError(
791 "The model has been loaded with `accelerate` and therefore cannot be moved to a specific device. Please "
792 "discard the `device` argument when creating your pipeline object."
ValueError: The model has been loaded with `accelerate` and therefore cannot be moved to a specific device. Please discard the `device` argument when creating your pipeline object.
Is it problem with Langchain?
### Idea or request for content:
_No response_
|
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| 1,910,224,649 |
PR_kwDOIPDwls5bDsr5
| 10,984 |
speed up enforce_stop_tokens helper function
|
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[] | null | 5 | 2023-09-24T12:53:26 | 2023-09-27T12:57:27 | 2023-09-27T12:29:30 |
CONTRIBUTOR
| null |
**Description:**
As long as `enforce_stop_tokens` returns a first occurrence, we can speed up the execution by setting the optional `maxsplit` parameter to 1.
Tag maintainer:
@agola11
@hwchase17
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
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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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| 1,910,108,891 |
I_kwDOIPDwls5x2fLb
| 10,982 |
I want to use LangChain in Vercel's ai-chatbot template
|
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[] | null | 4 | 2023-09-24T05:34:50 | 2023-12-27T16:01:32 | null |
NONE
| null |
Hi. I'm using [Vercel's ai-chatbot template](https://github.com/vercel-labs/ai-chatbot) to prototype my application. I was wondering how I can convert the chat api call👇🏾 to use LangChain. I've been able to find examples that work, but I don't know how to have it so that I still use Vercel's KV database with LangChain.
Here's the code I would like to modify. Thanks.
```Javascript
// app/api/chat/route.ts
import { kv } from '@vercel/kv'
import { OpenAIStream, StreamingTextResponse } from 'ai'
import { Configuration, OpenAIApi } from 'openai-edge'
import { auth } from '@/auth'
import { nanoid } from '@/lib/utils'
export const runtime = 'edge'
const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY
})
const openai = new OpenAIApi(configuration)
export async function POST(req: Request) {
const json = await req.json()
const { messages, previewToken } = json
const userId = (await auth())?.user.id
if (!userId) {
return new Response('Unauthorized', {
status: 401
})
}
if (previewToken) {
configuration.apiKey = previewToken
}
const res = await openai.createChatCompletion({
model: 'gpt-3.5-turbo',
messages,
temperature: 0.7,
stream: true
})
const stream = OpenAIStream(res, {
async onCompletion(completion) {
const title = json.messages[0].content.substring(0, 100)
const id = json.id ?? nanoid()
const createdAt = Date.now()
const path = `/chat/${id}`
const payload = {
id,
title,
userId,
createdAt,
path,
messages: [
...messages,
{
content: completion,
role: 'assistant'
}
]
}
await kv.hmset(`chat:${id}`, payload)
await kv.zadd(`user:chat:${userId}`, {
score: createdAt,
member: `chat:${id}`
})
}
})
return new StreamingTextResponse(stream)
}
```
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I_kwDOIPDwls5x2W5a
| 10,981 |
Issue: I want to load SitemapLoader but only 2023 lastmod pages.
|
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| false | null |
[] | null | 4 | 2023-09-24T03:16:22 | 2023-12-24T16:01:12 | null |
NONE
| null |
### Issue you'd like to raise.
Hi , I'm using the SiteMap Loader as a document Loader , and I want to filter the lastmod metadata for the 2023 year only and nothing else, is it possible?
Here is my code:
`# fixes a bug with asyncio and jupyter
import nest_asyncio
nest_asyncio.apply()
from langchain.document_loaders.sitemap import SitemapLoader
loader = SitemapLoader(
"https:www.example.ca.sitemap.xml",
filter_urls=["https://www.example.ca/en/"]
)
docs = loader.load()`
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5bDFpz
| 10,979 |
redirect mrkl
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[] | null | 1 | 2023-09-23T22:49:47 | 2023-09-23T23:09:14 | 2023-09-23T23:09:13 |
COLLABORATOR
| null | null |
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| 1,910,006,050 |
I_kwDOIPDwls5x2GEi
| 10,978 |
cant store embeddings from llama_cpp_python
|
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[] | null | 2 | 2023-09-23T21:29:01 | 2023-10-15T14:19:30 | null |
NONE
| null |
### System Info
Python==3.11.5
langchain==0.0.300
llama_cpp_python==0.2.6
chromadb==0.4.12
Running on Windows and on CPU
### Who can help?
@agola11
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Embeddings can't be stored in a chroma/FAISS vectorstore using llama-cpp-python.
I found that embedding the contents of very very simple websites, such as "http://example.org" work fine.
Here is the code I am executing in my notebook:
```
from langchain.document_loaders import WebBaseLoader
from langchain.embeddings import LlamaCppEmbeddings
from langchain.vectorstores import Chroma
llama = LlamaCppEmbeddings(model_path="../models/openorca_stx.gguf")
loader = WebBaseLoader("https://www.bbc.com/")
pages = loader.load()
vectordb = Chroma.from_documents(
documents=pages,
embedding=llama,
persist_directory='../data/vectorstores/'
)
```
Here is the Traceback:
```
ValueError Traceback (most recent call last)
[p:\git_repos\langchain-test\src_langchain\04_vectorstore.ipynb](file:///P:/git_repos/langchain-test/src_langchain/04_vectorstore.ipynb) Cell 8 line 1
[7](vscode-notebook-cell:/p%3A/git_repos/langchain-test/src_langchain/04_vectorstore.ipynb#X12sZmlsZQ%3D%3D?line=6) loader = WebBaseLoader("https://www.bbc.com/")
[8](vscode-notebook-cell:/p%3A/git_repos/langchain-test/src_langchain/04_vectorstore.ipynb#X12sZmlsZQ%3D%3D?line=7) pages = loader.load()
---> [10](vscode-notebook-cell:/p%3A/git_repos/langchain-test/src_langchain/04_vectorstore.ipynb#X12sZmlsZQ%3D%3D?line=9) vectordb = Chroma.from_documents(
[11](vscode-notebook-cell:/p%3A/git_repos/langchain-test/src_langchain/04_vectorstore.ipynb#X12sZmlsZQ%3D%3D?line=10) documents=pages,
[12](vscode-notebook-cell:/p%3A/git_repos/langchain-test/src_langchain/04_vectorstore.ipynb#X12sZmlsZQ%3D%3D?line=11) embedding=llama,
[13](vscode-notebook-cell:/p%3A/git_repos/langchain-test/src_langchain/04_vectorstore.ipynb#X12sZmlsZQ%3D%3D?line=12) persist_directory='../data/vectorstores/'
[14](vscode-notebook-cell:/p%3A/git_repos/langchain-test/src_langchain/04_vectorstore.ipynb#X12sZmlsZQ%3D%3D?line=13) )
File [d:\miniconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\chroma.py:646](file:///D:/miniconda3/envs/langchain/Lib/site-packages/langchain/vectorstores/chroma.py:646), in Chroma.from_documents(cls, documents, embedding, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs)
644 texts = [doc.page_content for doc in documents]
645 metadatas = [doc.metadata for doc in documents]
--> 646 return cls.from_texts(
647 texts=texts,
648 embedding=embedding,
649 metadatas=metadatas,
650 ids=ids,
651 collection_name=collection_name,
652 persist_directory=persist_directory,
653 client_settings=client_settings,
654 client=client,
655 collection_metadata=collection_metadata,
656 **kwargs,
...
--> 510 self.input_ids[self.n_tokens : self.n_tokens + n_tokens] = batch
511 # Save logits
512 rows = n_tokens if self.params.logits_all else 1
ValueError: could not broadcast input array from shape (8,) into shape (0,)
```
### Expected behavior
I expect this function `
Chroma.from_documents(
documents=pages,
embedding=llama,
persist_directory='../data/vectorstores/'
)
` to create document embeddings and store them in the persistent chromadb
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10674
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[] | null | 2 | 2023-09-23T19:48:32 | 2023-09-25T17:11:21 | 2023-09-23T23:11:52 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5x11tg
| 10,976 |
ReAct Agent doesn't execute
|
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[] | null | 4 | 2023-09-23T17:12:11 | 2023-12-30T16:04:58 | 2023-12-30T16:04:58 |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.300
Summary: Building applications with LLMs through composability
Home-page: https://github.com/langchain-ai/langchain
Author: None
Author-email: None
License: MIT
Location: /anaconda/envs/azureml_py38/lib/python3.8/site-packages
Requires: requests, jsonpatch, dataclasses-json, numpy, tenacity, numexpr, aiohttp, langsmith, anyio, SQLAlchemy, pydantic, async-timeout, PyYAML
Required-by: llama-index
### 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.llms import HuggingFaceTextGenInference
llm = HuggingFaceTextGenInference(
inference_server_url="http://x.x.x.x:8080/",
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.6,
# repetition_penalty=1.1,
)
from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.agents.react.base import DocstoreExplorer
docstore = DocstoreExplorer(vectordb)
tools = [
Tool(
name="Search",
func=docstore.search,
description="useful for when you need to ask with search",
),
Tool(
name="Lookup",
func=docstore.lookup,
description="useful for when you need to ask with lookup",
),
]
react = initialize_agent(tools, model, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
react.run('Punishment for drinking and driving')
```
Error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/azureuser/cloudfiles/code/Users/Tejasw/langchain_retreival.ipynb Cell 34 line 1
----> 1 react.run('Punishment for drinking and driving')
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/chains/base.py:487, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
485 if len(args) != 1:
486 raise ValueError("`run` supports only one positional argument.")
--> 487 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
488 _output_key
489 ]
491 if kwargs and not args:
492 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
493 _output_key
494 ]
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/chains/base.py:292, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
290 except BaseException as e:
291 run_manager.on_chain_error(e)
--> 292 raise e
293 run_manager.on_chain_end(outputs)
294 final_outputs: Dict[str, Any] = self.prep_outputs(
295 inputs, outputs, return_only_outputs
296 )
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/chains/base.py:286, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
279 run_manager = callback_manager.on_chain_start(
280 dumpd(self),
281 inputs,
282 name=run_name,
283 )
284 try:
285 outputs = (
--> 286 self._call(inputs, run_manager=run_manager)
287 if new_arg_supported
288 else self._call(inputs)
289 )
290 except BaseException as e:
291 run_manager.on_chain_error(e)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/agents/agent.py:1122, in AgentExecutor._call(self, inputs, run_manager)
1120 # We now enter the agent loop (until it returns something).
1121 while self._should_continue(iterations, time_elapsed):
-> 1122 next_step_output = self._take_next_step(
1123 name_to_tool_map,
1124 color_mapping,
1125 inputs,
1126 intermediate_steps,
1127 run_manager=run_manager,
1128 )
1129 if isinstance(next_step_output, AgentFinish):
1130 return self._return(
1131 next_step_output, intermediate_steps, run_manager=run_manager
1132 )
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/agents/agent.py:977, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
975 tool_run_kwargs["llm_prefix"] = ""
976 # We then call the tool on the tool input to get an observation
--> 977 observation = tool.run(
978 agent_action.tool_input,
979 verbose=self.verbose,
980 color=color,
981 callbacks=run_manager.get_child() if run_manager else None,
982 **tool_run_kwargs,
983 )
984 else:
985 tool_run_kwargs = self.agent.tool_run_logging_kwargs()
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/tools/base.py:360, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)
358 except (Exception, KeyboardInterrupt) as e:
359 run_manager.on_tool_error(e)
--> 360 raise e
361 else:
362 run_manager.on_tool_end(
363 str(observation), color=color, name=self.name, **kwargs
364 )
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/tools/base.py:332, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)
329 try:
330 tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
331 observation = (
--> 332 self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
333 if new_arg_supported
334 else self._run(*tool_args, **tool_kwargs)
335 )
336 except ToolException as e:
337 if not self.handle_tool_error:
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/tools/base.py:511, in Tool._run(self, run_manager, *args, **kwargs)
502 if self.func:
503 new_argument_supported = signature(self.func).parameters.get("callbacks")
504 return (
505 self.func(
506 *args,
507 callbacks=run_manager.get_child() if run_manager else None,
508 **kwargs,
509 )
510 if new_argument_supported
--> 511 else self.func(*args, **kwargs)
512 )
513 raise NotImplementedError("Tool does not support sync")
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/langchain/agents/react/base.py:77, in DocstoreExplorer.search(self, term)
75 def search(self, term: str) -> str:
76 """Search for a term in the docstore, and if found save."""
---> 77 result = self.docstore.search(term)
78 if isinstance(result, Document):
79 self.document = result
TypeError: search() missing 1 required positional argument: 'search_type'
```
### Expected behavior
Should successfully execute.
|
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PR_kwDOIPDwls5bC0Gr
| 10,975 |
Extended hugging face inference with text classification
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[] | null | 1 | 2023-09-23T16:51:32 | 2023-09-23T17:00:10 | null |
NONE
| null |
Extended with the current HuggingFaceEndpoint to support "text-classification" tasks. At the moment this doesn't support batch inference.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5bCsi5
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fix make-coverage local invocation #10941
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[] | null | 2 | 2023-09-23T14:38:02 | 2023-09-23T23:03:54 | 2023-09-23T23:03:54 |
CONTRIBUTOR
| null |
Fix the invocation of `make coverage` in `libs/langchain`
Fixes #10941
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PR_kwDOIPDwls5bCnth
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Support for computing embeddings using ONNX
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[] | null | 5 | 2023-09-23T13:12:07 | 2023-10-17T20:54:59 | null |
NONE
| null |
- **Description:** This PR introduces the inclusion of `OnnxEmbeddings` in the LangChain library. It allows users to compute embeddings for documents and queries using ONNX models. The change involves the creation of a new file `onnx.py` to house the `OnnxEmbeddings` class and the modification of the `__init__.py` file to include `OnnxEmbeddings` in the `__all__` list, making it accessible as part of the module's public API.
- **Issue:** N/A (New Feature)
- **Dependencies:** This change requires the `onnx`, `onnxruntime`, `optimum`, and `transformers` packages. Users who wish to utilize `OnnxEmbeddings` should ensure that these packages are installed. If not, they can install them using the following command:
```bash
pip install optimum transformers onnxruntime onnx
```
- **Tag Maintainer:** @baskaryan, @eyurtsev, @hwchase17
- **Twitter handle:** https://twitter.com/malteH_B
- **LinkedIn handle:** https://www.linkedin.com/in/maltehb/
### Implementation Details
The `OnnxEmbeddings` class inherits from `BaseModel` and `Embeddings`, and it overrides the `embed_documents` and `embed_query` methods to compute embeddings using ONNX models. It utilizes the `AutoTokenizer` from the `transformers` library for tokenization and `ORTModelForFeatureExtraction` from the `optimum.onnxruntime` package for feature extraction.
Currently, the model loaded from the `transformers` hub is just exported to ONNX format in-memory. One could imagine a flag for writing the ONNX model to disk, and then loading it.
The users have the flexibility to pass keyword arguments to the model and during tokenization through `model_kwargs` and `encode_kwargs` respectively. They can also set a `query_instruction`, which will prepend the query text during the embedding of queries. This is, of course, important if using models such as BGE or Instruct.
### Testing
Since this is a new feature, it is crucial to include tests for this integration. Tests should focus on the functionality of `OnnxEmbeddings`, ensuring that it computes the embeddings correctly and handles edge cases well. Preferably, unit tests that do not rely on network access should be written to maintain the reliability of the test suite.
### Examples
An example notebook showcasing the usage of `OnnxEmbeddings` should be included in the `docs/extras` directory to aid users in understanding how to use this new feature.
### Final Notes
This feature is an enhancement for LangChain, providing users with a significantly faster option for computing embeddings, compared to using `SentenceTransformers` and other similar libraries.
### Checklist
- I have run `make format` and `make lint` locally.
- The `make test` command did not work, due to another issue, which should be fixed in another PR.
I would appreciate it if @baskaryan, @eyurtsev, or @hwchase17 could review this PR and provide their valuable feedback.
|
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I_kwDOIPDwls5x1eeS
| 10,972 |
Extend HuggingFaceEndpoint Class to Support Text Classification Endpoints
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[] | null | 3 | 2023-09-23T11:59:43 | 2023-12-30T16:05:03 | 2023-12-30T16:05:03 |
NONE
| null |
### Feature request
The current HuggingFaceEndpoint class supports several tasks like text generation and summarization but lacks support for text classification. This feature request aims to extend the class to handle text classification tasks.
### Motivation
I am working on a project and I find myself having to trying to use langchain but it seems to be missing a text-classifcation is missing. I think it will be a great add as it allows us to chain text-classification tasks as well.
### Your contribution
I will make changes to the HuggingFaceEndpoint to allow anyone else to make a chain with text-classification task involved. At the moment this will not be for batch classification.
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PR_kwDOIPDwls5bCfUS
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Support for `ONNX` embeddings
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[] | null | 1 | 2023-09-23T10:53:17 | 2023-09-23T11:00:27 | 2023-09-23T11:00:27 |
NONE
| null |
- **Description:** This PR introduces the addition of a new file, `onnx.py`, in the `langchain/langchain/embeddings/` directory. The newly added file implements the `OnnxEmbeddings` class, which serves to compute document and query embeddings using an ONNX model. This class allows the user to initialize ONNX models and compute embeddings for given texts with the help of the ONNX Runtime. If the required dependencies are not met, the class will raise an informative ImportError, prompting the user to install the necessary packages.
- **Issue:** There is no issue to fix, other than supporting a significant speedup in embedding performance time.
- **Dependencies:**
- optimum.onnxruntime: This is required for the `ORTModelForFeatureExtraction` class used in initializing the ONNX model.
- transformers: Required for the `AutoTokenizer` used for tokenizing the input text before passing it to the model.
- **Tag Maintainer:** @hwchase17
- **Twitter Handle:** https://twitter.com/malteH_B
### Contribution Guidelines Compliance
- Code has been formatted using `make format`.
- Code is lint-free with `make lint`.
- Tests pass with `make test`.
|
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I_kwDOIPDwls5x1APQ
| 10,970 |
OutputParserException: Could not parse LLM output
|
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[] | null | 2 | 2023-09-23T05:24:56 | 2023-12-30T16:05:09 | 2023-12-30T16:05:08 |
NONE
| null |
### System Info
Doing experiment on google collab
using llam2 7b quantized model
below is my code
agent with memory & i tried A gentExecutor
```
source_text_tool = Tool(
name="The Prophet Source Text QA System",
func=prophet_qa_chain.run,
description="Useful when asked question related to philosophy or The Prophet."
)
analysis_text_tool = Tool(
name="Other philosophy QA System",
func=analysis_qa_chain.run,
description="Useful when asked questions related to philosophy or Stoicism or Sikhs"
)
prefix = """
You're having a conversation with a human. You're helpful and answering
questions to your maximum ability.You have access to the following tools:
"""
suffix = """Let's Go!"
{chat_history}
Question: {input}
{agent_scratchpad}
"""
tools = [source_text_tool, analysis_text_tool]
prompt = ZeroShotAgent.create_prompt(tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history","agent_scratchpad"]) #{chat_history}
memory = ConversationBufferMemory(memory_key="chat_history")
llm_chain = LLMChain(llm=load_llm(),
prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain,
tools=tools,
verbose=True)
agent_chain_memory = AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
memory=memory, #its without memo
verbose=True
)
```
error i im getting
```
> Entering new AgentExecutor chain...
---------------------------------------------------------------------------
OutputParserException Traceback (most recent call last)
[<ipython-input-31-a44cd2d0a1a6>](https://localhost:8080/#) in <cell line: 1>()
----> 1 agent_chain_memory.run(input="How should I handle my fear of death ?")
7 frames
[/usr/local/lib/python3.10/dist-packages/langchain/agents/mrkl/output_parser.py](https://localhost:8080/#) in parse(self, text)
50
51 if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
---> 52 raise OutputParserException(
53 f"Could not parse LLM output: `{text}`",
54 observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
OutputParserException: Could not parse LLM output: `*Thought:* "I will take a deep breath and reflect on the nature of mortality, considering both the transience of life and the certainty of death."
*Action Input:* Choose one of the following options:
A) Seek guidance from The Prophet Muhammad's teachings on the afterlife.
B) Explore Stoicism or Sikh philosophy for coping mechanisms.
C) Utilize a QA system specifically designed to address questions related to philosophy and death.
*Observation:* The action taken will determine the final answer provided.`
```
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
# i used documents to store embedding in chromdadb & using below data
analysis_qa_chain = RetrievalQA.from_chain_type(
llm=load_llm(),
chain_type="map_reduce",
retriever=web_db.as_retriever()
)
prophet_qa_chain = RetrievalQA.from_chain_type(
llm=load_llm(),
chain_type="map_reduce",
retriever=prophet_db.as_retriever()
)
source_text_tool = Tool(
name="The Prophet Source Text QA System",
func=prophet_qa_chain.run,
description="Useful when asked question related to philosophy or The Prophet."
)
analysis_text_tool = Tool(
name="Other philosophy QA System",
func=analysis_qa_chain.run,
description="Useful when asked questions related to philosophy or Stoicism or Sikhs"
)
prefix = """
You're having a conversation with a human. You're helpful and answering
questions to your maximum ability.You have access to the following tools:
"""
suffix = """Let's Go!"
{chat_history}
Question: {input}
{agent_scratchpad}
"""
tools = [source_text_tool, analysis_text_tool]
prompt = ZeroShotAgent.create_prompt(tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history","agent_scratchpad"]) #{chat_history}
memory = ConversationBufferMemory(memory_key="chat_history")
llm_chain = LLMChain(llm=load_llm(),
prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain,
tools=tools,
verbose=True)
agent_chain_memory = AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
memory=memory, #its without memo
verbose=True
)
```
```
agent_chain_memory.run(input="How should I handle my fear of death ?")
```
### Expected behavior
im getting op but error also coming with this
```
> Entering new AgentExecutor chain...
---------------------------------------------------------------------------
OutputParserException Traceback (most recent call last)
[<ipython-input-31-a44cd2d0a1a6>](https://localhost:8080/#) in <cell line: 1>()
----> 1 agent_chain_memory.run(input="How should I handle my fear of death ?")
7 frames
[/usr/local/lib/python3.10/dist-packages/langchain/agents/mrkl/output_parser.py](https://localhost:8080/#) in parse(self, text)
50
51 if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
---> 52 raise OutputParserException(
53 f"Could not parse LLM output: `{text}`",
54 observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
OutputParserException: Could not parse LLM output: `*Thought:* "I will take a deep breath and reflect on the nature of mortality, considering both the transience of life and the certainty of death."
*Action Input:* Choose one of the following options:
A) Seek guidance from The Prophet Muhammad's teachings on the afterlife.
B) Explore Stoicism or Sikh philosophy for coping mechanisms.
C) Utilize a QA system specifically designed to address questions related to philosophy and death.
*Observation:* The action taken will determine the final answer provided.`
```
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PR_kwDOIPDwls5bB4R7
| 10,969 |
[OpenSearch] Pass ids using from_texts and indexname in add_texts and search
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[] | null | 3 | 2023-09-23T00:03:05 | 2023-09-24T01:47:59 | 2023-09-23T23:12:51 |
CONTRIBUTOR
| null |
### Description
This PR makes the following changes to OpenSearch:
1. Pass optional ids with `from_texts`
2. Pass an optional index name with `add_texts` and `search` instead of using the same index name that was used during `from_texts`
### Issue
https://github.com/langchain-ai/langchain/issues/10967
### Maintainers
@rlancemartin, @eyurtsev, @navneet1v
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PR_kwDOIPDwls5bByOQ
| 10,968 |
Support for Gradient.ai embedding
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[] | null | 2 | 2023-09-22T22:59:11 | 2023-09-24T08:00:26 | 2023-09-23T23:10:23 |
CONTRIBUTOR
| null |
Adds support for gradient.ai's embedding model.
This will remain a Draft, as the code will likely be refactored with the `pip install gradientai` python sdk.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5x0hsg
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add support for passing of ids in from_texts in OpenSearch vector store
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[] | null | 0 | 2023-09-22T22:44:13 | 2023-10-10T16:29:37 | 2023-10-10T16:29:37 |
NONE
| null |
### Feature request
Currently in OpenSearch vector store while using the function `from_texts` it doesn't take `ids` as a parameter, so if multiple times the code is run for same documents the data gets duplicated. Which results in duplicated response being returned.
To fix this we just need to pass `ids` as optional parameter in the function. which should then be passed to add_embeddings function.
This is similar to what we are doing in add_texts function.
### Motivation
if multiple times the code is run for same documents the data gets duplicated. Which results in duplicated response being returned.
### Your contribution
@naveentatikonda who work on the integration can do this.
|
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PR_kwDOIPDwls5bBwHS
| 10,966 |
Fixes error with providers that don't have model_id
|
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[] | null | 2 | 2023-09-22T22:41:31 | 2023-09-23T22:34:29 | 2023-09-23T22:34:29 |
CONTRIBUTOR
| null |
## Description
Fixes error with using the chain for providers that don't have `model_id` field.

|
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| 1,909,592,111 |
I_kwDOIPDwls5x0hAv
| 10,965 |
Authentication to HuggingFaceTextGenInference API running on custom server
|
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[] | null | 2 | 2023-09-22T22:39:06 | 2023-12-30T16:05:14 | 2023-12-30T16:05:13 |
NONE
| null |
### Issue you'd like to raise.
Hello everyone,
I have HuggingFaceTextGenInference API running on my server with **authentication token**. I would like to call this API through Langchain. So, I am using the following code:
```
from langchain.llms import HuggingFaceTextGenInference
llm = HuggingFaceTextGenInference(
inference_server_url="url",
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.03,
)
```
My question is: how can we pass the **authentication token as argument** in order to call the API. The documentation is not clear on how to do that.
### Suggestion:
_No response_
|
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| 1,909,588,715 |
PR_kwDOIPDwls5bBvFi
| 10,964 |
Add metadata in embeddings for VectorStoreRetrieverMemory
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| false | null |
[] | null | 1 | 2023-09-22T22:33:42 | 2023-11-07T18:12:19 | null |
NONE
| null |
- **Description:** This PR allows to attach metadata to vector store embeddings when used as a retriever for the VectorStoreRetrieverMemory class,
- **Issue:** #9067,
- **Twitter handle:** [@rafacanvaz](https://twitter.com/rafacanvaz)
@hwchase17 @eyurtsev @baskaryan
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| 1,909,520,567 |
PR_kwDOIPDwls5bBgSQ
| 10,963 |
GradientLLM Docs update and model_id renaming.
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[] | null | 1 | 2023-09-22T21:07:35 | 2023-10-13T21:13:52 | 2023-10-13T20:57:58 |
CONTRIBUTOR
| null |
Related to #10800
- Errors in the Docstring of GradientLLM / Gradient.ai LLM
- Renamed the `model_id` to `model` and adapting this in all tests. Reason to so is to be in Sync with `GradientEmbeddings` and other LLM's.
- inmproving tests so they check the headers in the sent request.
- making the aiosession a private attribute in the docs, as in the future `pip install gradientai` will be replacing aiosession.
- adding a example how to fine-tune on the Prompt Template as suggested in #10800
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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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| 1,909,505,147 |
I_kwDOIPDwls5x0Lx7
| 10,962 |
I just got here from a Twitter link that a colleague sent me (https://twitter.com/llm_sec/status/1668711587287375876?s=20). I'm only a causal observer (not a Langchain user or contributor), but I thought it might be good to drop these links in case you're unaware of the ways that attackers can escape from AST-based Python "sandboxes":
|
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[] | null | 1 | 2023-09-22T20:51:22 | 2023-12-30T16:05:19 | 2023-12-30T16:05:18 |
NONE
| null |
I just got here from a Twitter link that a colleague sent me (https://twitter.com/llm_sec/status/1668711587287375876?s=20). I'm only a causal observer (not a Langchain user or contributor), but I thought it might be good to drop these links in case you're unaware of the ways that attackers can escape from AST-based Python "sandboxes":
https://hacktricks.boitatech.com.br/misc/basic-python/bypass-python-sandboxes
https://github.com/mahaloz/ctf-wiki-en/blob/master/docs/pwn/linux/sandbox/python-sandbox-escape.md
The strategies in these links aren't exhaustive, but hopefully illustrate that this style of sandboxing makes attacks more complex without defeating them entirely.
_Originally posted by @qxcv in https://github.com/langchain-ai/langchain/issues/6003#issuecomment-1590176952_
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I_kwDOIPDwls5x0LmB
| 10,961 |
SQLDatabaseChain. It's "azure-openai-chat LLM cannot be loaded
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[] | null | 13 | 2023-09-22T20:50:31 | 2023-12-20T12:42:50 | null |
NONE
| null |
### System Info
Hi @harrisoUnable to load the model logged using mlflow
```
import mlflow
logged_model = 'runs:/runid/sql_database_chain'
# Load model as a PyFuncModel.
loaded_model = mlflow.pyfunc.load_model(logged_model)
```
mlflow -> 2.7.2.dev0
langchain ->0.0.297
langchain-experimental -> 0.0.20
Exception:
`ValueError Traceback (most recent call last)
File <command-594169214443775>:5
2 logged_model = 'runs:/8998b4fd57c743fe8e0dae9a19ca5155/sql_database_chain'
4 # Load model as a PyFuncModel.
----> 5 loaded_model = mlflow.pyfunc.load_model(logged_model)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/mlflow/pyfunc/__init__.py:637, in load_model(model_uri, suppress_warnings, dst_path)
635 data_path = os.path.join(local_path, conf[DATA]) if (DATA in conf) else local_path
636 try:
--> 637 model_impl = importlib.import_module(conf[MAIN])._load_pyfunc(data_path)
638 except ModuleNotFoundError as e:
639 if conf[MAIN] == _DATABRICKS_FS_LOADER_MODULE:
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/mlflow/langchain/__init__.py:778, in _load_pyfunc(path)
773 """
774 Load PyFunc implementation for LangChain. Called by ``pyfunc.load_model``.
775 :param path: Local filesystem path to the MLflow Model with the ``langchain`` flavor.
776 """
777 wrapper_cls = _TestLangChainWrapper if _MLFLOW_TESTING.get() else _LangChainModelWrapper
--> 778 return wrapper_cls(_load_model_from_local_fs(path))
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/mlflow/langchain/__init__.py:807, in _load_model_from_local_fs(local_model_path)
804 model_type = flavor_conf.get(_MODEL_TYPE_KEY)
805 loader_arg = flavor_conf.get(_LOADER_ARG_KEY)
--> 807 return _load_model(
808 lc_model_path,
809 model_type,
810 loader_arg,
811 agent_model_path,
812 tools_model_path,
813 agent_primitive_path,
814 loader_fn_path,
815 persist_dir,
816 )
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/mlflow/langchain/__init__.py:660, in _load_model(path, model_type, loader_arg, agent_path, tools_path, agent_primitive_path, loader_fn_path, persist_dir)
658 model = _RetrieverChain.load(path, **kwargs).retriever
659 else:
--> 660 model = load_chain(path, **kwargs)
661 elif agent_path is None and tools_path is None:
662 model = load_chain(path)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/langchain/chains/loading.py:595, in load_chain(path, **kwargs)
593 return hub_result
594 else:
--> 595 return _load_chain_from_file(path, **kwargs)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/langchain/chains/loading.py:622, in _load_chain_from_file(file, **kwargs)
619 config["memory"] = kwargs.pop("memory")
621 # Load the chain from the config now.
--> 622 return load_chain_from_config(config, **kwargs)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/langchain/chains/loading.py:585, in load_chain_from_config(config, **kwargs)
582 raise ValueError(f"Loading {config_type} chain not supported")
584 chain_loader = type_to_loader_dict[config_type]
--> 585 return chain_loader(config, **kwargs)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/langchain/chains/loading.py:369, in _load_sql_database_chain(config, **kwargs)
367 if "llm_chain" in config:
368 llm_chain_config = config.pop("llm_chain")
--> 369 chain = load_chain_from_config(llm_chain_config)
370 return SQLDatabaseChain(llm_chain=chain, database=database, **config)
371 if "llm" in config:
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/langchain/chains/loading.py:585, in load_chain_from_config(config, **kwargs)
582 raise ValueError(f"Loading {config_type} chain not supported")
584 chain_loader = type_to_loader_dict[config_type]
--> 585 return chain_loader(config, **kwargs)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/langchain/chains/loading.py:41, in _load_llm_chain(config, **kwargs)
39 if "llm" in config:
40 llm_config = config.pop("llm")
---> 41 llm = load_llm_from_config(llm_config)
42 elif "llm_path" in config:
43 llm = load_llm(config.pop("llm_path"))
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-0d48d952-9c91-4092-be60-44e0bb77c25a/lib/python3.10/site-packages/langchain/llms/loading.py:19, in load_llm_from_config(config)
16 config_type = config.pop("_type")
18 if config_type not in type_to_cls_dict:
---> 19 raise ValueError(f"Loading {config_type} LLM not supported")
21 llm_cls = type_to_cls_dict[config_type]
22 return llm_cls(**config)
ValueError: Loading azure-openai-chat LLM not supported
`
### Who can help?
na
### 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
na
### Expected behavior
it should able to load the model
|
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| 1,909,495,362 |
PR_kwDOIPDwls5bBa06
| 10,960 |
Typo fix to MathpixPDFLoader - changed processed_file_format default …
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[] | null | 2 | 2023-09-22T20:40:48 | 2023-09-29T02:03:31 | 2023-09-29T02:03:30 |
CONTRIBUTOR
| null |
…from mmd to md. https://github.com/langchain-ai/langchain/issues/7282
<!--
- **Description:** minor fix to a breaking typo - MathPixPDFLoader processed_file_format is "mmd" by default, doesn't work, changing to "md" fixes the issue,
- **Issue:** 7282 (https://github.com/langchain-ai/langchain/issues/7282),
- **Dependencies:** none,
- **Tag maintainer:** @hwchase17,
- **Twitter handle:** none
-->
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PR_kwDOIPDwls5bBZez
| 10,959 |
LLMRails Embedding
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[] | null | 2 | 2023-09-22T20:34:14 | 2023-09-23T23:11:03 | 2023-09-23T23:11:03 |
CONTRIBUTOR
| null |
LLMRails Embedding Integration
This PR provides integration with LLMRails. Implemented here are:
langchain/embeddings/llm_rails.py
docs/extras/integrations/text_embedding/llm_rails.ipynb
Hi @hwchase17 after adding our vectorstore integration to langchain with confirmation of you and @baskaryan, now we want to add our embedding integration
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Bagatur/vertex scheduled test
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Add from_embeddings for opensearch
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PR_kwDOIPDwls5bBTF4
| 10,956 |
Update Serializable to use classmethods
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[] | null | 1 | 2023-09-22T20:04:38 | 2023-09-25T17:39:31 | 2023-09-25T17:39:30 |
COLLABORATOR
| null |
The goal of this PR is to update Serializable object to use classmethods rather than isinstance checks to determine:
(1) is the type serializable
(2) what is its namespace
Secerts and attributes can stay instance level properties
These both should be class level properties rather than instance level properties and will ease in impelementing validation based on type when loading data.
Goal:
```python
def loads(serialized_representation, allow_list=Sequence[Type[Serializable]]):
...
loads(..., [HumanMessage, AIMessage]) -> would only allow de-serialization of human message or AI message
```
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| 10,953 |
Issue: getting ValueError(f"One output key expected, got {outputs.keys()}") in a ConversationalRetrievalChain call
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[] | null | 3 | 2023-09-22T19:04:44 | 2023-10-06T17:19:41 | 2023-10-06T17:19:20 |
NONE
| null |
### Issue you'd like to raise.
Not sure this is the right place to raise this, but I'm having difficulty with the return value of ConversationalRetrievalChain (called "qa" in my code) when return_source_documents=True. The object returned is (I believe) a python dictionary object with two keys, "answer" and "source_documets". When I execute the folllwing line <output = self.qa({"question": question})> I get an error back that reads ValueError(f"One output key expected, got {outputs.keys()}").
Any help on why this is happening and how I can fix?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5bBE3c
| 10,952 |
fix integration tests
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PR_kwDOIPDwls5bA8za
| 10,951 |
vercel fix
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I_kwDOIPDwls5xzezj
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Issue: GPT4AllEmbeddings() throwing GGML Assert Error
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[] | null | 2 | 2023-09-22T18:04:19 | 2023-12-30T16:05:24 | 2023-12-30T16:05:23 |
NONE
| null |
### GPT4AllEmbeddings() throwing GGML Assert Error
I am trying to follow this use case
https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa
This was working till yesterday, Today its not working
```
from langchain.vectorstores import Chroma
from langchain.embeddings import GPT4AllEmbeddings
vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
```
Console error:
```
Found model file at C:\Users\saura\.cache\gpt4all\ggml-all-MiniLM-L6-v2-f16.bin
GGML_ASSERT: C:\Users\circleci\project\gpt4all-backend\llama.cpp-mainline\ggml.c:8027: ggml_nelements(dst) == ggml_nelements(src0)
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5bA0t5
| 10,948 |
Add a batch size for cleanup
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COLLABORATOR
| null |
Add pagination to indexing cleanup to deal with large numbers of documents that
need to be deleted.
|
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PR_kwDOIPDwls5bAszf
| 10,947 |
llm feat table revision
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| 1,909,235,868 |
PR_kwDOIPDwls5bAjvr
| 10,946 |
Updated `LocalAIEmbeddings` docstring to better explain why `openai`
|
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CONTRIBUTOR
| null |
Fixes my misgivings in https://github.com/langchain-ai/langchain/issues/10912
|
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PR_kwDOIPDwls5bAiKs
| 10,945 |
Support using async callback handlers with sync callback manager
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[] | null | 1 | 2023-09-22T16:47:53 | 2023-09-28T09:39:03 | 2023-09-28T09:39:02 |
COLLABORATOR
| null |
The current behaviour just calls the handler without awaiting the coroutine, which results in exceptions/warnings, and obviously doesn't actually execute whatever the callback handler does
<!-- 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,
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
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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|
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| 1,909,213,885 |
PR_kwDOIPDwls5bAfGy
| 10,944 |
table fix
|
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I_kwDOIPDwls5xy_qi
| 10,943 |
duplicate validator function
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[] | null | 4 | 2023-09-22T16:18:55 | 2023-09-22T22:10:44 | 2023-09-22T22:10:44 |
NONE
| null |
### System Info
Using langchain==0.0.299 in Ubuntu 22.04.1 LTS (GNU/Linux 5.19.0-1028-aws x86_64). I get the following error:
pydantic.errors.ConfigError: duplicate validator function "langchain.document_loaders.embaas.BaseEmbaasLoader.validate_environment"; if this is intended, set `allow_reuse=True`
I'm not importing pydantic separately, to make sure that I’m not creating this conflict. Nonetheless, I’m unable to resolve this unless this is updated in your code base. Thank you.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Simply importing LangChain is causing this error.
### Expected behavior
The library should import if there is no dependency or conflict with other libraries.
|
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| 1,909,165,621 |
I_kwDOIPDwls5xy441
| 10,942 |
Issue: (Chore) remove dependency on `isort`
|
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[] | null | 2 | 2023-09-22T16:00:02 | 2023-09-23T14:24:18 | 2023-09-23T14:24:17 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Ruff replaced isort but it's still in the `pyproject.toml` ; we can remove isort.
### Suggestion:
I can do it; just leaving this issue as a to-do
|
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| 1,909,163,325 |
I_kwDOIPDwls5xy4U9
| 10,941 |
Issue: `make coverage` doesn't work locally
|
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[] | null | 1 | 2023-09-22T15:58:24 | 2023-09-23T23:03:55 | 2023-09-23T23:03:55 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
When I set up the local environment and try to run `make coverage`, I get this error:
```bash
; make coverage
poetry run pytest --cov \
--cov-config=.coveragerc \
--cov-report xml \
--cov-report term-missing:skip-covered
================================================================================================================ test session starts ================================================================================================================
platform darwin -- Python 3.9.17, pytest-7.4.0, pluggy-1.2.0
rootdir: /Users/cjameson/workspace/cjcjameson/langchain/libs/langchain
configfile: pyproject.toml
plugins: asyncio-0.20.3, cov-4.1.0, vcr-1.0.2, syrupy-4.2.1, mock-3.11.1, anyio-3.7.1, dotenv-0.5.2, socket-0.6.0
asyncio: mode=strict
collected 2832 items / 1 error / 4 skipped
INTERNALERROR> Traceback (most recent call last):
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/_pytest/config/__init__.py", line 1611, in getoption
INTERNALERROR> val = getattr(self.option, name)
INTERNALERROR> AttributeError: 'Namespace' object has no attribute 'only_extended'
INTERNALERROR>
INTERNALERROR> The above exception was the direct cause of the following exception:
INTERNALERROR>
INTERNALERROR> Traceback (most recent call last):
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/_pytest/main.py", line 270, in wrap_session
INTERNALERROR> session.exitstatus = doit(config, session) or 0
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/_pytest/main.py", line 323, in _main
INTERNALERROR> config.hook.pytest_collection(session=session)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_hooks.py", line 433, in __call__
INTERNALERROR> return self._hookexec(self.name, self._hookimpls, kwargs, firstresult)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_manager.py", line 112, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_callers.py", line 155, in _multicall
INTERNALERROR> return outcome.get_result()
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_result.py", line 108, in get_result
INTERNALERROR> raise exc.with_traceback(exc.__traceback__)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_callers.py", line 80, in _multicall
INTERNALERROR> res = hook_impl.function(*args)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/_pytest/main.py", line 334, in pytest_collection
INTERNALERROR> session.perform_collect()
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/_pytest/main.py", line 672, in perform_collect
INTERNALERROR> hook.pytest_collection_modifyitems(
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_hooks.py", line 433, in __call__
INTERNALERROR> return self._hookexec(self.name, self._hookimpls, kwargs, firstresult)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_manager.py", line 112, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_callers.py", line 155, in _multicall
INTERNALERROR> return outcome.get_result()
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_result.py", line 108, in get_result
INTERNALERROR> raise exc.with_traceback(exc.__traceback__)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/pluggy/_callers.py", line 80, in _multicall
INTERNALERROR> res = hook_impl.function(*args)
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/libs/langchain/tests/unit_tests/conftest.py", line 43, in pytest_collection_modifyitems
INTERNALERROR> only_extended = config.getoption("--only-extended") or False
INTERNALERROR> File "/Users/cjameson/workspace/cjcjameson/langchain/venv/lib/python3.9/site-packages/_pytest/config/__init__.py", line 1622, in getoption
INTERNALERROR> raise ValueError(f"no option named {name!r}") from e
INTERNALERROR> ValueError: no option named 'only_extended'
====================================================================================================== 4 skipped, 1 warning, 1 error in 3.80s =======================================================================================================
make: *** [coverage] Error 3
```
### Suggestion:
It looks like the `pytest_addoption` in `tests/unit_tests/conftest.py` is not being found.
This stack-overflow attributes it to pytest not being able to find `conftest.py` files in nested directories. https://stackoverflow.com/a/31526934
The recommendations to create a plugin or move the conftest.py files don't seem palatable, but let me know if maybe that's the thing to do
Given the re-organization into `libs/langchain`, that could have messed up pytest local development.
I'm curious if/how it works in CI ...
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bump 300
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2. an example notebook showing its use. It lives in `docs/extras` directory.
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PR_kwDOIPDwls5bALuL
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In MergerRetriever async call all retrievers in parallel
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Remove python eval from vector sql db chain
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
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PR_kwDOIPDwls5a_1pI
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"afrom_texts" method implemented for vectordb base
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[] | null | 3 | 2023-09-22T14:29:33 | 2023-10-17T21:17:40 | 2023-10-17T21:17:13 |
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),
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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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| 1,908,982,916 |
PR_kwDOIPDwls5a_tqm
| 10,935 |
Accept run name arg for non-chain runs
|
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[] | null | 1 | 2023-09-22T14:08:13 | 2023-09-22T15:41:26 | 2023-09-22T15:41:25 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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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| 1,908,845,535 |
PR_kwDOIPDwls5a_P8V
| 10,934 |
small fixes to Vertex
|
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[] | null | 3 | 2023-09-22T12:47:36 | 2023-09-22T15:18:09 | 2023-09-22T15:18:09 |
CONTRIBUTOR
| null |
Fixed tests, updated the required version of the SDK and a few minor changes after the recent improvement (https://github.com/langchain-ai/langchain/pull/10910)
|
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PR_kwDOIPDwls5a_KRS
| 10,933 |
Aleph Alpha Async Embeddings (as a context manager)
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[] | null | 2 | 2023-09-22T12:30:02 | 2023-10-17T21:16:42 | null |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** Updated the Aleph Alpha Embeddings to support async calls. The async call is execured as a contex manager, example in the documentation
- **Issue:** Much faster execution of tha calls, cause we can send multiple calls at the same time
- **Dependencies:** no
- **Twitter handle:** None
Also added the docs showing how to use it.
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| 1,908,809,191 |
PR_kwDOIPDwls5a_IB-
| 10,932 |
Add ElasticsearchChatMessageHistory
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[] | null | 2 | 2023-09-22T12:23:03 | 2023-10-13T03:30:02 | 2023-10-12T23:51:38 |
CONTRIBUTOR
| null |
**Description**
This PR adds the `ElasticsearchChatMessageHistory` implementation that stores chat message history in the configured [Elasticsearch](https://www.elastic.co/elasticsearch/) deployment.
```python
from langchain.memory.chat_message_histories import ElasticsearchChatMessageHistory
history = ElasticsearchChatMessageHistory(
es_url="https://my-elasticsearch-deployment-url:9200", index="chat-history-index", session_id="123"
)
history.add_ai_message("This is me, the AI")
history.add_user_message("This is me, the human")
```
**Dependencies**
- [elasticsearch client](https://elasticsearch-py.readthedocs.io/) required
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TODO: **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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- **Dependencies:** any dependencies required for this change,
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls5xxKR-
| 10,929 |
OutputParserException: Could not parse LLM output: `The dataframe has a mix of integer and object data types.`
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[] | null | 2 | 2023-09-22T11:15:19 | 2023-12-30T16:05:29 | 2023-12-30T16:05:28 |
NONE
| null |
### System Info
When I ask the agent what kind of data does the pandas dataframe has. It gave me this error. I wanted to know what did the rows indicate and what could the LLM make out of the data. Like is it a financial data or some sales data.
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Load a data and ask what kind of data does the dataframe has
### Expected behavior
It should give the kind of data it was fed
|
{
"url": "https://api.github.com/repos/langchain-ai/langchain/issues/10929/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/langchain-ai/langchain/issues/10929/timeline
| null |
not_planned
| null | null |
https://api.github.com/repos/langchain-ai/langchain/issues/10928
|
https://api.github.com/repos/langchain-ai/langchain
|
https://api.github.com/repos/langchain-ai/langchain/issues/10928/labels{/name}
|
https://api.github.com/repos/langchain-ai/langchain/issues/10928/comments
|
https://api.github.com/repos/langchain-ai/langchain/issues/10928/events
|
https://github.com/langchain-ai/langchain/pull/10928
| 1,908,571,341 |
PR_kwDOIPDwls5a-Tym
| 10,928 |
Fix default refine prompt template bug
|
{
"login": "Neronuser",
"id": 8417184,
"node_id": "MDQ6VXNlcjg0MTcxODQ=",
"avatar_url": "https://avatars.githubusercontent.com/u/8417184?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/Neronuser",
"html_url": "https://github.com/Neronuser",
"followers_url": "https://api.github.com/users/Neronuser/followers",
"following_url": "https://api.github.com/users/Neronuser/following{/other_user}",
"gists_url": "https://api.github.com/users/Neronuser/gists{/gist_id}",
"starred_url": "https://api.github.com/users/Neronuser/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/Neronuser/subscriptions",
"organizations_url": "https://api.github.com/users/Neronuser/orgs",
"repos_url": "https://api.github.com/users/Neronuser/repos",
"events_url": "https://api.github.com/users/Neronuser/events{/privacy}",
"received_events_url": "https://api.github.com/users/Neronuser/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 5454193895,
"node_id": "LA_kwDOIPDwls8AAAABRRhk5w",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/lgtm",
"name": "lgtm",
"color": "0E8A16",
"default": false,
"description": ""
},
{
"id": 5680700839,
"node_id": "LA_kwDOIPDwls8AAAABUpidpw",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/auto:bug",
"name": "auto:bug",
"color": "E99695",
"default": false,
"description": "Related to a bug, vulnerability, unexpected error with an existing feature"
}
] |
closed
| false | null |
[] | null | 1 | 2023-09-22T09:44:24 | 2023-09-22T15:04:28 | 2023-09-22T15:04:28 |
CONTRIBUTOR
| null |
**Description:**
Default refine template does not actually use the refine template defined above, it uses a string with the variable name.
@baskaryan, @eyurtsev, @hwchase17
|
{
"url": "https://api.github.com/repos/langchain-ai/langchain/issues/10928/reactions",
"total_count": 1,
"+1": 1,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/langchain-ai/langchain/issues/10928/timeline
| null | null | false |
{
"url": "https://api.github.com/repos/langchain-ai/langchain/pulls/10928",
"html_url": "https://github.com/langchain-ai/langchain/pull/10928",
"diff_url": "https://github.com/langchain-ai/langchain/pull/10928.diff",
"patch_url": "https://github.com/langchain-ai/langchain/pull/10928.patch",
"merged_at": "2023-09-22T15:04:28"
}
|
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