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I_kwDOIPDwls52x5T2
| 13,349 |
Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 6 | 2023-11-14T13:53:39 | 2023-11-14T14:32:33 | null |
NONE
| null |
### Issue you'd like to raise.
I have my code and for the first section of it I am inputting a memory key based in the session_id as a UUID
I am receiving the following error: Any idea what the issue is?
> Traceback (most recent call last):
> File "/Users/habuhassan004/Desktop/VIK/10x-csv-chat/llm.py", line 62, in <module>
> print(chat_csv(session_id,None,'Hi how are you?'))
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/habuhassan004/Desktop/VIK/10x-csv-chat/llm.py", line 37, in chat_csv
> response = conversation({"question": question})
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/habuhassan004/Desktop/VIK/10x-csv-chat/venv/lib/python3.11/site-packages/langchain/chains/base.py", line 286, in __call__
> inputs = self.prep_inputs(inputs)
> ^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/habuhassan004/Desktop/VIK/10x-csv-chat/venv/lib/python3.11/site-packages/langchain/chains/base.py", line 443, in prep_inputs
> self._validate_inputs(inputs)
> File "/Users/habuhassan004/Desktop/VIK/10x-csv-chat/venv/lib/python3.11/site-packages/langchain/chains/base.py", line 195, in _validate_inputs
> raise ValueError(f"Missing some input keys: {missing_keys}")
> ValueError: Missing some input keys: {'session_id'}
here is the input:
`session_id = uuid.uuid4()
print(chat_csv(session_id,None,'Hi how are you?'))`
Here is the code:
```
def chat_csv(
session_id: UUID = None,
file_path: str = None,
question: str = None
):
# session_id = str(session_id)
memory = ConversationBufferMemory(
memory_key=str(session_id),
return_messages=True
)
if file_path == None:
template = """You are a nice chatbot having a conversation with a human.
Previous conversation:
{session_id}
New human question: {question}
Response:"""
prompt = PromptTemplate.from_template(template)
conversation = LLMChain(
llm=OpenAI(temperature=0),
prompt=prompt,
verbose=False,
memory=memory
)
response = conversation({"question": question})
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5fZsuz
| 13,347 |
Fix lintand errors
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[] | null | 1 | 2023-11-14T11:37:51 | 2023-11-14T11:39:04 | 2023-11-14T11:38:45 |
NONE
| null | null |
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PR_kwDOIPDwls5fZsBb
| 13,346 |
Make it easier to subclass RunnableEach
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[] | null | 1 | 2023-11-14T11:35:38 | 2023-11-15T13:12:58 | 2023-11-15T13:12:57 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
|
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[] | null | 3 | 2023-11-14T11:02:21 | 2023-11-14T11:29:17 | null |
NONE
| null |
### Issue you'd like to raise.
```
llm = ChatOpenAI(
openai_api_key= os.environ["OPENAI_API_KEY"],
temperature=0,
streaming=True,
callbacks=[AsyncIteratorCallbackHandler()]
)
embeddings = OpenAIEmbeddings()
loader = TextLoader("static/faq/ecommerce_faq.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
docsearch = Chroma.from_documents(texts, embeddings)
faq_chain = RetrievalQA.from_chain_type(
llm,
chain_type="stuff",
retriever=docsearch.as_retriever(),
)
async def call_openai(question):
callback = AsyncIteratorCallbackHandler()
# coroutine = wait_done(model.agenerate(messages=[[HumanMessage(content=question)]]), callback.done)
coroutine = wait_done(faq_chain.arun(question), callback.done)
task = asyncio.create_task(coroutine)
async for token in callback.aiter():
print('token', token)
yield f"data: {token}\n\n"
await task
app = FastAPI()
@app.get("/")
async def homepage():
return FileResponse('static/index.html')
@app.post("/ask")
def ask(body: dict):
return StreamingResponse(call_openai(body['question']), media_type="text/event-stream")
if __name__ == "__main__":
uvicorn.run(host="127.0.0.1", port=8888, app=app)
```
I can run it normally using model.agenerate, but it cannot run after using faq_chain.arun. what is the reason for this?
### Suggestion:
_No response_
|
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| 1,992,482,519 |
I_kwDOIPDwls52wt7X
| 13,343 |
autoawq for vllm
|
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open
| false | null |
[] | null | 3 | 2023-11-14T10:47:09 | 2023-11-26T19:17:39 | null |
NONE
| null |
### Feature request
Is there a way to autoawq support for vllm, Im 'setting quantization to 'awq' but its not working
### Motivation
faster inference
### Your contribution
N/A
|
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I_kwDOIPDwls52wjaF
| 13,342 |
PowerPoint loader crashing
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[] | null | 8 | 2023-11-14T10:24:09 | 2023-11-19T15:48:20 | null |
NONE
| null |
### System Info
Langchain Version: 0.0.335
Platform: Windows 10
Python Version = 3.11.3
IDE: VS Code
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
MWE:
A PowerPoint presentation named "test.pptx" in the same folder as the script. Content is a title slide with sample text.
```python
from langchain.document_loaders import UnstructuredPowerPointLoader
def ingest_docs():
loader= UnstructuredPowerPointLoader('test.pptx')
docs = loader.load()
return docs
```
I get a problem in 2/3 tested environments:
1. Running the above MWE with `ingest_docs()` in a simple python script will yield no problem. The content of the PowerPoint (text on the title slide) is displayed.
2. Running the above MWE in a Jupyter Notebook with `ingest_docs()` will cause the cell to run indefinetely. Trying to interrupt the kernel results in: `Interrupting the kernel timed out`. A fix is to restart the kernel.
3. Running the MWE in Streamlit (see code below) will result the spawned server to die immediately. (The cmd Window simply closes)
```python
import streamlit as st
from langchain.document_loaders import UnstructuredPowerPointLoader
def ingest_docs():
[as above]
st.write(ingest_docs())
```
### Expected behavior
I expect the MWE to work the same in the Notebook and Streamlit environments just as in the simple Python script.
|
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I_kwDOIPDwls52wENL
| 13,340 |
Why no constructor (init function) in Langchain while pydantics are everywhere?
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[] | null | 2 | 2023-11-14T09:14:09 | 2023-11-14T09:24:14 | null |
NONE
| null |
### Issue you'd like to raise.
Hi all. Can anyone help to answer why there's no init function (constructor) for classes in LangChain, while Pydantic occurs everywhere. It seems hard for IDEs to jump to related codes. Is that one kind of anti-pattern of Python programming?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5fYgif
| 13,338 |
add more powerfull clean html function
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[] | null | 1 | 2023-11-14T08:20:44 | 2023-12-05T11:00:19 | 2023-12-05T11:00:19 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** I add a function to scape html the default transform function can't scape my webside data and this worked function ,
- **Issue:** Can't scape webside data my webside ["https://www.presight.io/privacy-policy.html"]# it fixed
- **Dependencies:** BeautifulSoup,NavigableString from bs4 and re,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** Yes
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,992,202,637 |
I_kwDOIPDwls52vpmN
| 13,336 |
DOC: Wrong documentation link
|
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[] | null | 1 | 2023-11-14T08:10:40 | 2023-11-14T08:11:41 | null |
NONE
| null |
### Issue with current documentation:
Hi all, on main website [langchain.com](https://www.langchain.com/), above the folder, to the right of hero section both boxes (python and js) point to the [python docs](https://python.langchain.com/docs/get_started/introduction).
### Idea or request for content:
It's better and clearer if each block points to the related lang. Of course, it's not urgent or critical :)
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PR_kwDOIPDwls5fYYHu
| 13,335 |
docs: Install langsmith from conda-forge
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[] | null | 2 | 2023-11-14T07:52:00 | 2023-11-29T08:25:15 | 2023-11-29T03:44:03 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
langsmith is available on conda-forge as well and also a dependency of the package so it gets installed either way by conda https://github.com/conda-forge/langchain-feedstock/blob/306ed13308c051716ced37a28962a0ac80a9aa7f/recipe/meta.yaml#L43
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| 1,992,174,212 |
I_kwDOIPDwls52viqE
| 13,334 |
Add POST API Functionality to LangChain
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[] | null | 4 | 2023-11-14T07:49:34 | 2023-11-15T11:59:16 | 2023-11-15T11:59:16 |
NONE
| null |
### Feature request
LangChain supports GET functions, but there is no support for POST functions. This feature request proposes the addition of POST API functionality to enhance the capabilities of LangChain.
### Motivation
The motivation behind this feature request is to extend the capabilities of LangChain to handle not only GET requests but also POST requests.
### Your contribution
I am willing to contribute to the development of this feature. I will carefully follow the contributing guidelines and provide a pull request to implement the POST API functionality.
|
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I_kwDOIPDwls52vJD6
| 13,333 |
Issue: <How to do streaming response from Ollama??>
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[] | null | 2 | 2023-11-14T06:21:20 | 2023-11-15T11:30:03 | 2023-11-15T11:30:03 |
NONE
| null |
### Issue you'd like to raise.
I couldnt find any documentation on this , please help
How can i stream the response from Ollama ?
### Suggestion:
_No response_
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I_kwDOIPDwls52u63q
| 13,332 |
Reduce embeddings size
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[] | null | 2 | 2023-11-14T05:19:21 | 2023-11-14T05:26:04 | null |
NONE
| null |
### Issue you'd like to raise.
I'm trying to replicate [this example](https://python.langchain.com/docs/integrations/vectorstores/elasticsearch#basic-example) of langchain. I'm using ElasticSearch as the database to store the embedding. In the given example I have replaced `embeddings = OpenAIEmbeddings()` with `embeddings = OllamaEmbeddings(model="llama2")` which one can import `from langchain.embeddings import OllamaEmbeddings`. I'm running `Ollama` locally. But, I'm running into below error:
```
raise HTTP_EXCEPTIONS.get(status_code, TransportError)(
elasticsearch.exceptions.RequestError: RequestError(400, 'mapper_parsing_exception', 'The number of dimensions for field [vector] should be in the range [1, 2048] but was [4096]')
```
The Ollama model always create the embedding of size `4096` even when I set the chunk size of `500`. Is there any way to reduce the size of embedding? or is there anyway to store larger size embeddings in `ElasticSearch`
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5fXr8L
| 13,331 |
Adding langchain vectorstore for SurrealDB
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[] | null | 14 | 2023-11-14T04:33:09 | 2023-12-15T21:56:17 | 2023-12-15T21:34:52 |
CONTRIBUTOR
| null |
**Description:** Vectorstore implementation around [SurrealDB](https://www.surrealdb.com)
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PR_kwDOIPDwls5fXq7h
| 13,330 |
Adds OllamaFunctions wrapper
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[] | null | 2 | 2023-11-14T04:28:56 | 2023-12-01T14:54:00 | 2023-12-01T00:13:57 |
CONTRIBUTOR
| null |
CC @baskaryan @hwchase17 @jmorganca
Having a bit of trouble importing `langchain_experimental` from a notebook, will figure it out tomorrow
~Ah and also is blocked by #13226~
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PR_kwDOIPDwls5fXqBl
| 13,329 |
arxiv retrieval agent improvement
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PR_kwDOIPDwls5fXp9d
| 13,328 |
Add missing doc key to metadata field in AzureSearch Vectorstore
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[] | null | 4 | 2023-11-14T04:22:34 | 2023-12-05T03:53:28 | 2023-12-05T03:53:27 |
CONTRIBUTOR
| null |
- **Description:** Adds doc key to metadata field when adding document to Azure Search.
- **Issue:** -,
- **Dependencies:** -,
- **Tag maintainer:** @eyurtsev,
- **Twitter handle:** @finnless
Right now the document key with the name FIELDS_ID is not included in the FIELDS_METADATA field, and therefore is not included in the Document returned from a query. This is really annoying if you want to be able to modify that item in the vectorstore.
Other's thoughts on this are welcome.
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add more reasonable arxiv retriever
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[] | null | 1 | 2023-11-14T04:12:19 | 2023-11-14T04:54:16 | 2023-11-14T04:54:15 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls52usa2
| 13,326 |
Enhancing iMessageChatLoader to prevent skipping messages with extractable content
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[] | null | 0 | 2023-11-14T04:08:46 | 2023-11-28T20:45:45 | 2023-11-28T20:45:44 |
CONTRIBUTOR
| null |
### Feature request
Implement extraction of message content whenever iMessageChatLoader skips messages with null `text` field even though the content is present in other fields.
### Motivation
Due to iMessage's database schema change in its new MacOS Ventura, newer messages now have content encoded in the `attributedBody` body, with the `text` field being null.
This issue was originally raised by @idvorkin in Issue #10680.
### Your contribution
We intend to submit a pull request for this issue close to the end of November.
|
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I_kwDOIPDwls52upJD
| 13,325 |
SQLAlchemy version
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[] | null | 2 | 2023-11-14T03:56:20 | 2023-11-14T04:03:30 | null |
NONE
| null |
I have a web app and the SQLAlchemy is 1.x , when I use langchain which needs SQLAlchemy 2.x , it‘s diffcult for me to update the SQLAlchemy to 2.x，how can I fix this problem
|
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PR_kwDOIPDwls5fXkgC
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Update the imports for various exceptions in the ChatOpenAI class to be consistent with OpenAI API
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NONE
| null |
- **Description**: Update the imports for various exceptions in the ChatOpenAI class to be consistent with OpenAI API.
- **Issue**: https://github.com/langchain-ai/langchain/issues/13323,
- **Dependencies**: openai==1.2.3
- **Tag** maintainer: @hwchase17
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| 13,323 |
OpenAI exception classes have different import path in openai 1.2.3, causing breaking change in ChatOpenAI - Simple fix
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[] | null | 1 | 2023-11-14T03:41:42 | 2023-11-14T14:46:32 | null |
NONE
| null |
### System Info
- Langchain version: langchain==0.0.335
- OpenAI version: openai==1.2.3
- Platform: Darwin MacBook Pro 22.4.0 Darwin Kernel Version 22.4.0: Mon Mar 6 21:00:41 PST 2023; root:xnu-8796.101.5~3/RELEASE_ARM64_T8103 arm64
- Python version: Python 3.9.6
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Example script:
```python
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI()
user_input = input("Input prompt: ")
response = llm.invoke(user_input)
```
```python
return self._generate(
File "...lib/python3.9/site-packages/langchain/chat_models/openai.py", line 360, in _generate
response = self.completion_with_retry(
File "...lib/python3.9/site-packages/langchain/chat_models/openai.py", line 293, in completion_with_retry
retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
File "...lib/python3.9/site-packages/langchain/chat_models/openai.py", line 73, in _create_retry_decorator
openai.error.Timeout,
Exception module 'openai' has no attribute 'error'
```
Cause appears to be in `langchain.chat_models.openai`: https://github.com/langchain-ai/langchain/blob/5a920e14c06735441a9ea28c1313f8bd433dc721/libs/langchain/langchain/chat_models/openai.py#L82-L88
Modifying above to this appears to resolve the problem:
```python
errors = [
openai.Timeout,
openai.APIError,
openai.APIConnectionError,
openai.RateLimitError,
openai.ServiceUnavailableError,
]
```
### Expected behavior
Chat model invocation should return a BaseMessage.
|
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PR_kwDOIPDwls5fXe4U
| 13,322 |
Added llama2-13b-chat-v1 on bedrock
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[] | null | 9 | 2023-11-14T03:21:18 | 2023-11-29T19:42:57 | 2023-11-29T19:42:57 |
NONE
| null |
- **Description:** Added `llama2-13b-chat-v1` for Bedrock client
- **Issue:** #13316
- **Dependencies:** None
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5fXcqh
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Implemented MongoDB Atlas Self-Query Retriever
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[] | null | 7 | 2023-11-14T03:07:46 | 2023-11-30T15:49:09 | 2023-11-30T03:05:06 |
CONTRIBUTOR
| null |
# Description
This PR implements Self-Query Retriever for MongoDB Atlas vector store.
I've implemented the comparators and operators that are supported by MongoDB Atlas vector store according to the section titled "Atlas Vector Search Pre-Filter" from https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/.
Namely:
```
allowed_comparators = [
Comparator.EQ,
Comparator.NE,
Comparator.GT,
Comparator.GTE,
Comparator.LT,
Comparator.LTE,
Comparator.IN,
Comparator.NIN,
]
"""Subset of allowed logical operators."""
allowed_operators = [
Operator.AND,
Operator.OR
]
```
Translations from comparators/operators to MongoDB Atlas filter operators(you can find the syntax in the "Atlas Vector Search Pre-Filter" section from the previous link) are done using the following dictionary:
```
map_dict = {
Operator.AND: "$and",
Operator.OR: "$or",
Comparator.EQ: "$eq",
Comparator.NE: "$ne",
Comparator.GTE: "$gte",
Comparator.LTE: "$lte",
Comparator.LT: "$lt",
Comparator.GT: "$gt",
Comparator.IN: "$in",
Comparator.NIN: "$nin",
}
```
In visit_structured_query() the filters are passed as "pre_filter" and not "filter" as in the MongoDB link above since langchain's implementation of MongoDB atlas vector store(libs\langchain\langchain\vectorstores\mongodb_atlas.py) in _similarity_search_with_score() sets the "filter" key to have the value of the "pre_filter" argument.
```
params["filter"] = pre_filter
```
Test cases and documentation have also been added.
# Issue
#11616
# Dependencies
No new dependencies have been added.
# Documentation
I have created the notebook mongodb_atlas_self_query.ipynb outlining the steps to get the self-query mechanism working.
I worked closely with [@Farhan-Faisal](https://github.com/Farhan-Faisal) on this PR.
|
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PR_kwDOIPDwls5fXb95
| 13,320 |
feat: add ERNIE-Bot-4 Function Calling
|
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[] | null | 2 | 2023-11-14T03:03:20 | 2023-11-20T06:36:13 | 2023-11-20T06:36:13 |
CONTRIBUTOR
| null |
- **Description:** ERNIE-Bot-Chat-4 Large Language Model adds the ability of `Function Calling` by passing parameters through the `functions` parameter in the request. To simplify function calling for ERNIE-Bot-Chat-4, the `create_ernie_fn_chain()` function has been added. The definition and usage of the `create_ernie_fn_chain()` function is similar to that of the `create_openai_fn_chain()` function.
Examples as the follows:
```
import json
from langchain.chains.ernie_functions import (
create_ernie_fn_chain,
)
from langchain.chat_models import ErnieBotChat
from langchain.prompts import ChatPromptTemplate
def get_current_news(location: str) -> str:
"""Get the current news based on the location.'
Args:
location (str): The location to query.
Returs:
str: Current news based on the location.
"""
news_info = {
"location": location,
"news": [
"I have a Book.",
"It's a nice day, today."
]
}
return json.dumps(news_info)
def get_current_weather(location: str, unit: str="celsius") -> str:
"""Get the current weather in a given location
Args:
location (str): location of the weather.
unit (str): unit of the tempuature.
Returns:
str: weather in the given location.
"""
weather_info = {
"location": location,
"temperature": "27",
"unit": unit,
"forecast": ["sunny", "windy"],
}
return json.dumps(weather_info)
llm = ErnieBotChat(model_name="ERNIE-Bot-4")
prompt = ChatPromptTemplate.from_messages(
[
("human", "{query}"),
]
)
chain = create_ernie_fn_chain([get_current_weather, get_current_news], llm, prompt, verbose=True)
res = chain.run("北京今天的新闻是什么？")
print(res)
```
The running results of the above program are shown below：
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 北京今天的新闻是什么？
> Finished chain.
{'name': 'get_current_news', 'thoughts': '用户想要知道北京今天的新闻。我可以使用get_current_news工具来获取这些信息。', 'arguments': {'location': '北京'}}
```
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Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5fXEb0
| 13,319 |
Bagatur/update rag use case
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[] | null | 1 | 2023-11-14T01:26:20 | 2023-11-15T18:54:16 | 2023-11-15T18:54:15 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5fXBP8
| 13,318 |
Framework for supporting more languages in LanguageParser
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[] | null | 3 | 2023-11-14T01:15:09 | 2024-01-08T19:19:26 | null |
NONE
| null |
## Description
I am submitting this for a school project as part of a team of 5. Other team members are @LeilaChr, @maazh10, @Megabear137, @jelalalamy. This PR also has contributions from community members @Harrolee and @Mario928.
Initial context is in the issue we opened (#11229).
This pull request adds:
- Generic framework for expanding the languages that `LanguageParser` can handle, using the [tree-sitter](https://github.com/tree-sitter/py-tree-sitter#py-tree-sitter) parsing library and existing language-specific parsers written for it
- Support for the following additional languages in `LanguageParser`:
- C
- C++
- C#
- Go
- Java (contributed by @Mario928 https://github.com/ThatsJustCheesy/langchain/pull/2)
- Kotlin
- Lua
- Perl
- Ruby
- Rust
- Scala
- TypeScript (contributed by @Harrolee https://github.com/ThatsJustCheesy/langchain/pull/1)
Here is the [design document](https://docs.google.com/document/d/17dB14cKCWAaiTeSeBtxHpoVPGKrsPye8W0o_WClz2kk) if curious, but no need to read it.
## Issues
- Closes #11229
- Closes #10996
- Closes #8405
## Dependencies
`tree_sitter` and `tree_sitter_languages` on PyPI. We have tried to add these as optional dependencies.
## Documentation
We have updated the list of supported languages, and also added a section to `source_code.ipynb` detailing how to add support for additional languages using our framework.
## Maintainer
- @hwchase17 (previously reviewed https://github.com/langchain-ai/langchain/pull/6486)
Thanks!!
## Git commits
We will gladly squash any/all of our commits (esp merge commits) if necessary. Let us know if this is desirable, or if you will be squash-merging anyway.
<!-- 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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2. an example notebook showing its use. It lives in `docs/extras` directory.
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-->
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PR_kwDOIPDwls5fXAOf
| 13,317 |
add retrieval agent
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I_kwDOIPDwls52tzs0
| 13,316 |
Add support for Bedrock Llama 2 13b model (meta.llama2-13b-chat-v1)
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[] | null | 3 | 2023-11-14T00:34:45 | 2023-11-16T11:30:09 | null |
NONE
| null |
### System Info
I get following error just by adding model parameters to existing code that works with other models
"Malformed input request: 2 schema violations found, please reformat your input and try again."
```
model_name = "meta.llama2-13b-chat-v1"
model_kwargs = {
"max_gen_len": 512,
"temperature": 0.2,
"top_p": 0.9
}
bedrock_boto = boto3.client("bedrock-runtime", "us-east-1")
bedrock_llm = Bedrock(model_id=model_name, client=bedrock_boto,model_kwargs=model_kwargs)
bedrock_llm("Hello!")
```
### Who can help?
@hwchase17
@agola11
### 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
1. Attempt to call the new llama2 bedrock model like so:
```
model_name = "meta.llama2-13b-chat-v1"
model_kwargs = {
"max_gen_len": 512,
"temperature": 0.2,
"top_p": 0.9
}
bedrock_boto = boto3.client("bedrock-runtime", "us-east-1")
bedrock_llm = Bedrock(model_id=model_name, client=bedrock_boto,model_kwargs=model_kwargs)
bedrock_llm("Hello!")
```
### Expected behavior
The Bedrock class would work successfully as it does for other BedRock models
|
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PR_kwDOIPDwls5fWwNo
| 13,315 |
Runnable Context Provider
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[] | null | 5 | 2023-11-14T00:18:30 | 2023-12-01T09:52:27 | 2023-12-01T09:52:27 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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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.
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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.
-->
(Note: still WIP, would appreciate some feedback from maintainer)
**Description**: This PR adds a new core Runnable component called `RunnableContextProvider`. The motivation behind this runnable is that sometimes when writing long and complex chains, developers need to pass some core piece of data across multiple stages of the chain. For example, when working with a naive RAG where the retriever retrieves context (say `List[str]` for simplicity), one common case is to pass the retrieved `context` and original `question` as part of the output (say for the sake of doing evals or other data manipulations). The original way of achieving this might look like below:
```python
retriever = RunnableLambda(lambda _: ["doc 1 ...", ..., "doc n"]) # mock retriever
retrieval_chain = {
"context": retriever,
"question": RunnablePassthrough(),
}
format_chain = {
"original_context": itemgetter("context"),
"formatted_context": itemgetter("context") | RunnableLambda(lambda x: "## Documents" + "\n\n".join(x)),
"question": itemgetter("question"),
}
generation_chain = {
"result": {
"question": itemgetter("question"),
"formatted_context": itemgetter("formatted_context")
} | prompt | llm,
"context": itemgetter("original_context"),
"question": itemgetter("question"),
}
rag_chain = retrieval_chain | format_chain | generation_chain
```
Look at how complex and unreadable the chain becomes even for a naive RAG example. Most of the complexity is due to passing extra data around, which adds lots of `itemgetter` and data `Passthrough` which should be unnecessary.
`RunnableContextProvider` solves this issue by allowing data sharing across different stages of chain without having to explicitly wire up the data connection pipeline. Here's the basic API usage for implementing the same naive RAG as above:
```python
from langchain.schema.runnable import RunnableContextProvider
format_context = RunnableLambda(lambda x: "## Documents" + "\n\n".join(x))
rag_chain = RunnableContextProvider(
lambda getter, setter: {
"question": RunnablePassthrough() | setter("question"),
"formatted_context": retriever | setter("context") | format_context,
}
| prompt
| llm
| {
"result": RunnablePassthrough(),
"context": getter("context"),
"question": getter("question"),
}
)
```
- `getter`: an instance of `RunnableContextGetter`, which retrieves data from a key-value source automatically initialized in the background. It's a `Runnable` that outputs the value retrieved from the shared key-value source identified by the given key (input to `RunnableContextGetter` is ignored).
- `setter`: an instance of `RunnableContextSetter`, which updates value into the shared source given the key. The value written into the key is the output from the previous piped Runnable chain. Output from `RunnableContextSetter` is connected to the output of the chain immediately before the `setter`. Therefore, in the case of
```python
retriever | setter("context") | format_context
```
The lambda `format_context` would take the output from `retriever` as its input.
- batching: `batch` and `abatch` work out-of-box. Each single chain call inside the batch has a unique source. In other words, chains across different batch call do not share the same source ideally.
Also, support the decorator's pattern.
```python
@context_provider
def rag_chain(getter, setter):
retriever = RunnableLambda(lambda _: ["doc 1 ...", ..., "doc n"]) # mock retriever
format_context = RunnableLambda(lambda x: "## Documents" + "\n\n".join(x))
return (
{
"question": RunnablePassthrough() | setter("question"),
"formatted_context": retriever | setter("context") | format_context,
}
| prompt
| llm
| {
"result": RunnablePassthrough(),
"context": getter("context"),
"question": getter("question"),
}
)
rag_chain.invoke(...)
```
## Some Improvement Considerations
- Allows `setter` to set multiple keys based on the same input. Potential API usage:
```python
retriever | setter(lambda x: {
"list_context": x,
"string_context": "\n\n".join(x),
"context_size": len(x)
}) | format_context
```
- Naming: should we rename `getter` and `setter` to be `inject` and `provide` instead?
- Whether we should allow each key to be set only once.
- One caveat of `RunnableContextProvider` is that both `getter` and `setter` on the same key cannot appear inside the `RunnableParallel` because the order of execution is not guaranteed.
**Issue**: None.
**Dependencies**: None.
**Tag Maintainers**: @nfcampos
**Twitter handle**: [will add in the future]
|
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PR_kwDOIPDwls5fWsIk
| 13,314 |
Fix typo/line break in the middle of a word
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[] | null | 1 | 2023-11-13T23:56:32 | 2023-11-18T00:43:43 | 2023-11-18T00:43:42 |
CONTRIBUTOR
| null |
- **Description:** a simple typo/extra line break fix
- **Dependencies:** none
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PR_kwDOIPDwls5fWqW5
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IMPROVEMENT more research-assistant configurability
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[] | null | 1 | 2023-11-13T23:46:45 | 2023-11-15T05:04:58 | 2023-11-15T05:04:57 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5fWciW
| 13,311 |
Add a `ListRerank` document compressor
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[] | null | 3 | 2023-11-13T22:41:47 | 2023-12-05T17:05:52 | null |
NONE
| null |
- **Description:** This PR adds a new document compressor called `ListRerank`. It's derived from `BaseDocumentCompressor`. It's a near exact implementation of introduced by this paper: [Zero-Shot Listwise Document Reranking with a Large Language Model](https://arxiv.org/pdf/2305.02156.pdf) which it finds to outperform pointwise reranking, which is somewhat implemented in LangChain as [LLMChainFilter](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/chain_filter.py).
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @hwchase17 @izzymsft
- **Twitter handle:** @HarrisEMitchell
Notes:
1. I didn't add anything to `docs`. I wasn't exactly sure which patterns to follow as [cohere reranker is under Retrievers](https://python.langchain.com/docs/integrations/retrievers/cohere-reranker) with other external document retrieval integrations, but other contextual compression is [here](https://python.langchain.com/docs/modules/data_connection/retrievers/contextual_compression/). Happy to contribute to either with some direction.
2. I followed syntax, docstrings, implementation patterns, etc. as well as I could looking at nearby modules. One thing I didn't do was put the default prompt in a separate `.py` file like [Chain Filter](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/chain_filter_prompt.py) and [Chain Extract](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/chain_extract_prompt.py). Happy to follow that pattern if it would be preferred.
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| 1,991,600,818 |
PR_kwDOIPDwls5fWbZF
| 13,310 |
Remove deprecated param and flexibility for prompt
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[] | null | 6 | 2023-11-13T22:36:47 | 2023-11-29T19:48:25 | 2023-11-29T19:48:25 |
CONTRIBUTOR
| null |
- **Description:** Updated to remove deprecated parameter penalty_alpha, and use string variation of prompt rather than json object for better flexibility. - **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** N/A
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** @symbldotai
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I_kwDOIPDwls52tTjP
| 13,309 |
Pinecone SelfQueryRetriever with datetime filter
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[] | null | 0 | 2023-11-13T22:25:18 | 2023-11-13T22:25:33 | null |
NONE
| null |
### Feature request
I wan´t to use Pinecone SelfQueryRetriever with a metadata in datetime format. When I try to ask anything with a date show the error Bad Request.
I already change my class PineconeTranslator with this code with the same pattern of Scale, change the translator class. Since Pinecone just accept integer values and they ask you to use epoch format.
langchain\retrievers\self_query\pinecone.py
Class PineconeTranslator
` def visit_comparison(self, comparison: Comparison) -> Dict:
value = comparison.value
# convert timestamp to float as epoch format.
if type(value) is date:
value = time.mktime(value.timetuple())
return {
comparison.attribute: {
self._format_func(comparison.comparator): value
}
}`
### Motivation
I want to use selfretriever with pinecone and langchain the error limit the use of langchain with pinecone.
### Your contribution
Yes, I already made the changes in my machine like the code in the description
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PR_kwDOIPDwls5fWWlI
| 13,308 |
IMPROVEMENT format notebooks
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[] | null | 2 | 2023-11-13T22:17:17 | 2023-11-13T22:29:29 | 2023-11-13T22:21:26 |
COLLABORATOR
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PR_kwDOIPDwls5fWSP1
| 13,307 |
fix litellm openai imports
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[] | null | 1 | 2023-11-13T22:02:01 | 2023-11-14T01:55:11 | 2023-11-14T01:55:11 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls52tLha
| 13,306 |
Ollama LLM: Implement async functionality
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[] | null | 4 | 2023-11-13T21:59:16 | 2023-11-13T22:28:52 | null |
NONE
| null |
### Feature request
Implement Async methods in ollama llm and chat model classes.
### Motivation
Ollama implementation doesn't include the async methods _astream and _agenerate and i cannot create a async agent...
### Your contribution
This is my first issue, i can try but i am working in 3 different projects right now...
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reopened
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| 1,991,534,746 |
PR_kwDOIPDwls5fWM37
| 13,305 |
IMPROVEMENT self-query template
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[] | null | 1 | 2023-11-13T21:43:49 | 2023-11-13T22:03:17 | 2023-11-13T22:03:16 |
COLLABORATOR
| null |
- [ ] https://github.com/langchain-ai/langchain/pull/12694#discussion_r1391334719 -> keep date
- [x] https://github.com/langchain-ai/langchain/pull/12694#discussion_r1391336586
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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DOCS: cleanup docs directory
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Same change as #8403 but in other libs
also updates (c) LangChain Inc. instead of @hwchase17
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CONTRIBUTOR
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- **Description:** fix a bug that prevented as_retriever() in Vectara to use the desired input arguments
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[] | null | 1 | 2023-11-13T18:56:17 | 2023-11-14T01:37:55 | 2023-11-14T01:37:54 |
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PR_kwDOIPDwls5fVAm-
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Fix issue where response_if_no_docs_found is not implemented on async…
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[] | null | 1 | 2023-11-13T18:09:00 | 2023-11-30T03:06:13 | 2023-11-30T03:06:13 |
CONTRIBUTOR
| null |
Response_if_no_docs_found is not implemented in ConversationalRetrievalChain for async code paths. Implemented it and added test cases
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PR_kwDOIPDwls5fUwKl
| 13,296 |
Update error message in evaluation runner
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I_kwDOIPDwls52rUNe
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Langchain Template for RAG using Opensearch
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[] | null | 0 | 2023-11-13T16:58:01 | 2023-11-13T16:58:19 | null |
NONE
| null |
### Feature request
We have several RAG templates in [langchain template](https://github.com/langchain-ai/langchain/tree/master/templates) using other vector DB except Opensearch.
We need to add a similar template for Opensearch also as opensearch support the same feature.
As a first step we will start with OpenAI and then we expand this to other LLMs including Bedrock.
### Motivation
Opensearch supports this feature and it will be super helpful for opensearch users.
### Your contribution
I will take the initiative to raise PR on this issue.
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PR_kwDOIPDwls5fUOVW
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Add rag google vertex ai search template
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CONTRIBUTOR
| null |
- **Description:** This is a template demonstrating how to utilize Google Vertex AI Search in conjunction with ChatVertexAI()
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PR_kwDOIPDwls5fT2kp
| 13,293 |
fixed a concurrency bug in abatch
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CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** made `abatch` actually concurrent
- **Issue:** it processed batch sequentially before
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| 1,990,699,489 |
I_kwDOIPDwls52p6nh
| 13,291 |
Context retrieval
|
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[] | null | 2 | 2023-11-13T13:48:33 | 2023-11-13T13:59:24 | null |
NONE
| null |
### System Info
Linux and langchain
### 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
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am currently implementing a RAG chatbot using `ConversationBufferMemory` and `ConversationalRetrievalChain`as follow:
```
memory = ConversationBufferMemory(
memory_key="chat_history",
output_key="answer",
return_messages=True,
)
```
chain = ConversationalRetrievalChain.from_llm(
llm,
chain_type="stuff",
retriever=db.as_retriever(search_kwargs={"k": 1}),
memory=memory,
combine_docs_chain_kwargs={"prompt": PROMPT},
return_source_documents=True,
verbose=True,
)`
Where PROMPT is a prompt template that inputs chat_history, context and question.
### Expected behavior
I would like the retrieval chain and memory chain to keep track of the context i.e. instead of updating the context with the retrieved documents every time a user input a new question, I can keep track of the context.
My end goal is to have a function that takes into the old context, validate if retrieval of new documents is necessary, and if those documents can replace the new context or they need to be append to the old context. Is there a way to do this?
|
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| 1,990,492,825 |
PR_kwDOIPDwls5fSoa8
| 13,290 |
Improve openai callback
|
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[] | null | 1 | 2023-11-13T11:48:54 | 2023-11-14T08:20:13 | null |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
As we discussed in #12994 the current callback only returns 0 when the cost fails to calculate. This way it will raise an error instead to prevent users to falsely relying on an incorrect cost. I also improved the copy functions so that the actually create a new instance of the object and does not just return the same object.
|
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| 1,990,442,921 |
I_kwDOIPDwls52o7-p
| 13,289 |
Issue: Create async streaming custom LLM
|
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[] | null | 5 | 2023-11-13T11:20:02 | 2023-11-13T19:06:48 | null |
NONE
| null |
### Issue you'd like to raise.
I currently have an OLLAMA model running locally. I aim to develop a CustomLLM model that supports asynchronous streaming to interface with this model. However, the OLLAMA adapter provided by LangChain lacks support for these specific features. How can I accomplish this integration?
### Suggestion:
_No response_
|
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| 1,990,415,478 |
I_kwDOIPDwls52o1R2
| 13,288 |
Incorporate Memory inside ElasticsearchDatabaseChain
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[] | null | 5 | 2023-11-13T11:02:36 | 2023-11-14T07:58:36 | null |
NONE
| null |
### Issue you'd like to raise.
I am working with an elastic search index running locally via a docker container. Now I want to incorporate memory so that an elastic search query can be handled just like a chat. How can I do that so that every time the question I asked from the previous answers? It should prepare the es query accordingly.
Here is the sample code:
```
from elasticsearch import Elasticsearch
from langchain.chat_models import ChatOpenAI
from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain
import os
import openai
from dotenv import load_dotenv
import json
from langchain.prompts import HumanMessagePromptTemplate, AIMessagePromptTemplate
from langchain.schema import HumanMessage, AIMessage
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
load_dotenv()
os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')
ELASTIC_SEARCH_SERVER = "http://localhost:9200"
db = Elasticsearch(ELASTIC_SEARCH_SERVER)
memory = ConversationBufferMemory(memory_key="chat_history")
PROMPT_TEMPLATE = """Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.
Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.
Use the following format:
Question: Question here
ESQuery: Elasticsearch Query formatted as json
"""
PROMPT_SUFFIX = """Only use the following Elasticsearch indices:
{indices_info}
Question: {input}
ESQuery:"""
query_Prompt=PROMPT_TEMPLATE + PROMPT_SUFFIX
PROMPT = PromptTemplate.from_template(
query_Prompt,
)
DEFAULT_ANSWER_TEMPLATE = """Given an input question and relevant data from a database, answer the user question.
Only provide answer to me.
Use the following format:
Question: {input}
Data: {data}
Answer:"""
ANSWER_PROMPT = PromptTemplate.from_template(DEFAULT_ANSWER_TEMPLATE)
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
chain = ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, query_prompt=PROMPT, answer_prompt=ANSWER_PROMPT ,verbose=True, memory=memory)
while True:
# Get user input
user_input = input("Ask a question (or type 'exit' to end): ")
if user_input.lower() == 'exit':
break
# Invoke the chat model with the user's question
chain_answer = chain.invoke(user_input)
```
What could be the solution? Currently, it is not working if the following question is related to the previous answer.
### Suggestion:
_No response_
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PR_kwDOIPDwls5fRo-v
| 13,287 |
add cookbook for RAG with baidu QIANFAN and elasticsearch
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CONTRIBUTOR
| null |
**Description:**
Add cookbook for RAG with baidu QIANFAN and elasticsearch.
|
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| 1,990,163,579 |
I_kwDOIPDwls52n3x7
| 13,286 |
Support for Text Embedding Inference (TEI) from HuggingFace
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[] | null | 2 | 2023-11-13T08:52:10 | 2023-11-29T04:18:42 | null |
NONE
| null |
### Feature request
Similar to Text Generation Inference (TGI) for LLMs, HuggingFace created an inference server for text embeddings models called Text Embedding Inference (TEI).
See: https://github.com/huggingface/text-embeddings-inference
Could you integrate TEI into the supported LangChain text embedding models or do you guys already plan to do this?
### Motivation
We currently develop a rag based chat app and plan to deploy the components as microservices (LLM, DB, Embedding Model). Currently the only other suitable solution for us would be to use SagemakerEndpointEmbeddings. However being able to use TEI would be a great benefit.
### Your contribution
I work as an ML Engineer and could probably assist in some way if necessary.
|
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PR_kwDOIPDwls5fRciQ
| 13,285 |
Update index.mdx
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[] | null | 1 | 2023-11-13T08:41:40 | 2023-11-30T03:06:34 | 2023-11-30T03:06:34 |
CONTRIBUTOR
| null |
grammar correction
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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I_kwDOIPDwls52nvbj
| 13,284 |
NotFoundError: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}} when using openai==1.2.3 and langchain==0.0.335
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[] | null | 11 | 2023-11-13T08:30:39 | 2023-12-27T11:16:43 | null |
NONE
| null |
### System Info
from langchain.chat_models import AzureChatOpenAI
llm_chat = AzureChatOpenAI(deployment_name="gpt-4_32k", model_name = 'gpt-4-32k', openai_api_version=openai.api_version, temperature=0)
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
prompt = PromptTemplate(
input_variables = ["text"],
template="{text}"
)
llmchain = LLMChain(llm=llm_chat, prompt=prompt)
llmchain.run(text)
NotFoundError: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
openai==1.2.3 and langchain==0.0.335
from langchain.chat_models import AzureChatOpenAI
text = "Where is Germany located?"
llm_chat = AzureChatOpenAI(deployment_name="gpt-4_32k", model_name = 'gpt-4-32k', openai_api_version=openai.api_version, temperature=0)
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
prompt = PromptTemplate(
input_variables = ["text"],
template="{text}"
)
llmchain = LLMChain(llm=llm_chat, prompt=prompt)
llmchain.run(text)
### Expected behavior
In europe
|
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I_kwDOIPDwls52noqc
| 13,283 |
Replacing the correct code with GPT-4 will result in an error
|
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[] | null | 2 | 2023-11-13T08:16:48 | 2023-11-13T08:27:28 | null |
NONE
| null |
### Issue you'd like to raise.
**my code：**
from langchain.document_loaders import PyPDFLoader, TextLoader, PyMuPDFLoader
from langchain.chat_models import ChatOpenAI
from langchain.chains import QAGenerationChain
from langchain.prompts.prompt import PromptTemplate
loader_pdf = PyMuPDFLoader("/Users/11.pdf")
doc_pdf = loader_pdf.load()
llm = ChatOpenAI(temperature=0.1,model_name="gpt-3.5-turbo-16k")
templ = """You are a smart assistant designed to help high school teachers come up with reading comprehension questions.
Given a piece of text, you must come up with a question and answer pair that can be used to test a student's reading comprehension abilities.
When coming up with this question/answer pair, you must respond in the following format in Chinese:
```
{{
"question": "$YOUR_QUESTION_HERE",
"answer": "$THE_ANSWER_HERE"
}}
```
Everything between the ``` must be valid json.
Please come up with a question/answer pair, in the specified JSON format, for the following text:
----------------
{text}"""
prompt = PromptTemplate.from_template(templ)
chain = QAGenerationChain.from_llm(llm=llm, prompt=prompt)
doc_ = ''
for i in doc_pdf:
doc_ += i.page_content
print(doc_)
qa_pdf = chain.run(doc_)
This code can run correctly and have results.
But when “model_name='gpt-4' ”, an error is reported.
What is it???
**the error：**
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
Cell In[22], line 1
----> 1 qa_pdf = chain.run(doc_)
File ~/anaconda3/envs/langchain-py311/lib/python3.11/site-packages/langchain/chains/base.py:505, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
503 if len(args) != 1:
504 raise ValueError("`run` supports only one positional argument.")
--> 505 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
506 _output_key
507 ]
509 if kwargs and not args:
510 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
511 _output_key
512 ]
File ~/anaconda3/envs/langchain-py311/lib/python3.11/site-packages/langchain/chains/base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
313 inputs, outputs, return_only_outputs
314 )
File ~/anaconda3/envs/langchain-py311/lib/python3.11/site-packages/langchain/chains/base.py:304, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
297 run_manager = callback_manager.on_chain_start(
298 dumpd(self),
299 inputs,
300 name=run_name,
301 )
302 try:
303 outputs = (
--> 304 self._call(inputs, run_manager=run_manager)
305 if new_arg_supported
306 else self._call(inputs)
307 )
308 except BaseException as e:
309 run_manager.on_chain_error(e)
File ~/anaconda3/envs/langchain-py311/lib/python3.11/site-packages/langchain/chains/qa_generation/base.py:75, in QAGenerationChain._call(self, inputs, run_manager)
71 docs = self.text_splitter.create_documents([inputs[self.input_key]])
72 results = self.llm_chain.generate(
73 [{"text": d.page_content} for d in docs], run_manager=run_manager
74 )
---> 75 print(results)
76 qa = [json.loads(res[0].text) for res in results.generations]
77 return {self.output_key: qa}
File ~/anaconda3/envs/langchain-py311/lib/python3.11/site-packages/langchain/chains/qa_generation/base.py:75, in <listcomp>(.0)
71 docs = self.text_splitter.create_documents([inputs[self.input_key]])
72 results = self.llm_chain.generate(
73 [{"text": d.page_content} for d in docs], run_manager=run_manager
74 )
---> 75 print(results)
76 qa = [json.loads(res[0].text) for res in results.generations]
77 return {self.output_key: qa}
File ~/anaconda3/envs/langchain-py311/lib/python3.11/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
341 s = s.decode(detect_encoding(s), 'surrogatepass')
343 if (cls is None and object_hook is None and
344 parse_int is None and parse_float is None and
345 parse_constant is None and object_pairs_hook is None and not kw):
--> 346 return _default_decoder.decode(s)
347 if cls is None:
348 cls = JSONDecoder
File ~/anaconda3/envs/langchain-py311/lib/python3.11/json/decoder.py:337, in JSONDecoder.decode(self, s, _w)
332 def decode(self, s, _w=WHITESPACE.match):
333 """Return the Python representation of ``s`` (a ``str`` instance
334 containing a JSON document).
335
336 """
--> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
338 end = _w(s, end).end()
339 if end != len(s):
File ~/anaconda3/envs/langchain-py311/lib/python3.11/json/decoder.py:355, in JSONDecoder.raw_decode(self, s, idx)
353 obj, end = self.scan_once(s, idx)
354 except StopIteration as err:
--> 355 raise JSONDecodeError("Expecting value", s, err.value) from None
356 return obj, end
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
### Suggestion:
_No response_
|
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| 1,990,059,585 |
I_kwDOIPDwls52neZB
| 13,282 |
Use "parse_file" in class "ConversationBufferMemory" raise a ValidationError
|
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[] | null | 6 | 2023-11-13T07:47:40 | 2023-11-28T10:45:19 | null |
NONE
| null |
### System Info
Platform:Windows
LangChain:0.0.335
python:3.9.12
### Who can help?
@eyurtsev @hwchase17 @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here is my code:
from langchain.memory import ConversationBufferMemory
import langchain.load
ChatMemory = ConversationBufferMemory()
ChatMemory.chat_memory.add_ai_message("Hello")
ChatMemory.chat_memory.add_user_message("HI")
tojson = ChatMemory.json()
with open('E:\\memory.json', 'w') as file:
file.write(tojson)
file.close()
ParserChatMemory = ConversationBufferMemory.parse_file('E:\\memory.json')
### Expected behavior
Regarding the parse_file method in ConversationBufferMemory, there is not much introduction in the document, and I have not found a similar problem description. However, saving Memory into a file/or generating it from a file is a normal requirement, but when I execute parse_file This method throws a "ValidationError", the complete description is as follows:
ValidationError: 1 validation error for ConversationBufferMemory
chat_memory
instance of BaseChatMessageHistory expected (type=type_error.arbitrary_type; expected_arbitrary_type=BaseChatMessageHistory)
If there is any violation of my operation, please tell me, thank you very much
|
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| 1,990,054,092 |
I_kwDOIPDwls52ndDM
| 13,281 |
Utilize metadata during retrieval in ConversationalRetrieval and PGvector
|
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[] | null | 3 | 2023-11-13T07:43:07 | 2023-11-15T14:08:06 | null |
NONE
| null |
I am using PGvector as retriever in my Conversational Retriever Chain.
in my document there is some meta information saved like topic, keywords etc.
how can use this meta information in retrieval instead of only using content?
Would appreciate any solution for this.
|
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| 1,990,051,152 |
PR_kwDOIPDwls5fRH_p
| 13,280 |
Ollama pass kwargs as options instead of top
|
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[] | null | 2 | 2023-11-13T07:40:40 | 2023-11-13T18:28:48 | 2023-11-13T18:28:47 |
COLLABORATOR
| null |
Noticed params are really in `options` instead while reviewing #12895
|
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| 1,990,021,102 |
I_kwDOIPDwls52nU_u
| 13,279 |
qa retrieval
|
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[] | null | 4 | 2023-11-13T07:15:53 | 2023-11-13T07:40:28 | null |
NONE
| null |
### Issue you'd like to raise.
i should qa over an document using retrieval. i should not use any large language model. i can only use embedding model. here is my code:
```
# import
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from silly import no_ssl_verification
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
with no_ssl_verification():
# load the document and split it into chunks
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# hfemb = HuggingFaceEmbeddings()
# load it into Chroma
db = Chroma.from_documents(docs, embedding_function)
# query it
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
# print results
print(docs[0].page_content)
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
```
how can i change this code with not using any llm in "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())"
### Suggestion:
_No response_
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| 1,989,993,127 |
I_kwDOIPDwls52nOKn
| 13,278 |
retrieval embedding
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NONE
| null |
### Issue you'd like to raise.
i fix the code as following:
```
# import
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from silly import no_ssl_verification
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
with no_ssl_verification():
# load the document and split it into chunks
loader = TextLoader("paul_graham/paul_graham_essay.txt")
documents = loader.load()
# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# hfemb = HuggingFaceEmbeddings()
# load it into Chroma
db = Chroma.from_documents(docs, embedding_function)
# query it
query = "What were the two main things the author worked on before college?"
docs = db.similarity_search(query)
# print results
print(docs[0].page_content)
```
i get following output:
"I was nervous about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking work was very rare, and I didn't want to have to program in another language, which in those days would have meant C++ if I was lucky. So with my unerring nose for financial opportunity, I decided to write another book on Lisp. This would be a popular book, the sort of book that could be used as a textbook. I imagined myself living frugally off the royalties and spending all my time painting. (The painting on the cover of this book, ANSI Common Lisp, is one that I painted around this time.)
The best thing about New York for me was the presence of Idelle and Julian Weber. Idelle Weber was a painter, one of the early photorealists, and I'd taken her painting class at Harvard. I've never known a teacher more beloved by her students. Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant.
She liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn't that much older than me, and was super rich. The thought suddenly occurred to me: why don't I become rich? Then I'll be able to work on whatever I want.
Meanwhile I'd been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I'd seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet."
bu i should get "Before college the two main things I worked on, outside of school, were writing and programming."
### Suggestion:
_No response_
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| 1,989,982,161 |
I_kwDOIPDwls52nLfR
| 13,277 |
how to use SQLDatabaseSequentialChain
|
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[] | null | 2 | 2023-11-13T06:38:26 | 2023-11-13T06:44:21 | null |
NONE
| null |
how can I use SQLDatabaseSequentialChain to reduce the number of tokens for llms，can you give me some example or demo，I dont find useful info from the docs
|
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| 1,989,960,245 |
I_kwDOIPDwls52nGI1
| 13,276 |
turkish embedding
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[] | null | 5 | 2023-11-13T06:15:51 | 2023-11-30T15:23:57 | null |
NONE
| null |
### Issue you'd like to raise.
i have the following code for q&a system with retrieval mechanism:
```
# import
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from silly import no_ssl_verification
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
with no_ssl_verification():
# load the document and split it into chunks
loader = TextLoader("paul_graham/paul_graham_essay.txt")
documents = loader.load()
# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# hfemb = HuggingFaceEmbeddings()
# load it into Chroma
db = Chroma.from_documents(docs, embedding_function)
# query it
query = "What were the two main things the author worked on before college?"
docs = db.similarity_search(query)
# print results
print(docs[0].page_content)
```
i should do this retrieval in turkish dataset. so i should use turkish embeddings. how can i do that in my code?
### Suggestion:
_No response_
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I_kwDOIPDwls52nDsa
| 13,275 |
FAISS save_local / load_local don't be aware of avx2
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[] | null | 4 | 2023-11-13T06:05:42 | 2023-11-13T06:23:55 | null |
CONTRIBUTOR
| null |
### System Info
```
cat /proc/version
=> Linux version 5.15.107+
cat /etc/debian_version
=> 10.13
import langchain
langchain.__version__
=> '0.0.334'
```
I spent some time to debug why the function signature `search` is different between linux / macos m1.
I found that in macos, because it has arm arch, doesn't have swigfaiss_avx2.py file on download wheel.
Besides, in linux, because it has amd64 arch, which support avx2, has swigfaiss_avx2.py.
Here's the amd64 example:
If I make faiss vector store using `from_documents` like
```py
from langchain.embeddings import OpenAIEmbeddings
docs = [] # some documents
vs = FAISS.from_documents(docs, OpenAIEmbeddings())
vs.index
# <faiss.swigfaiss_avx2.IndexFlatL2; proxy of <Swig Object of type 'faiss::IndexFlatL2 *' at> >
vs.index.search
<bound method handle_Index.<locals>.replacement_search of <faiss.swigfaiss_avx2.IndexFlatL2; proxy of <Swig Object of type 'faiss::IndexFlatL2 *' at> >>
```
It is delived from swigfaiss_avx2's IndexFlatL2, and it's search method is replaced by `replacement_search` in faiss.__init__.py intentionally.
But, when we save this to local and load like:
```py
vs.save_local("faiss", "abcd.index")
vs2 = FAISS.load_local("faiss", OpenAIEmbeddings(), "abcd.index")
vs2.index
<faiss.swigfaiss.IndexFlat; proxy of <Swig Object of type 'faiss::IndexFlat *' at> >
vs2.index.search
<bound method IndexFlat.search of <faiss.swigfaiss.IndexFlat; proxy of <Swig Object of type 'faiss::IndexFlat *' at> >>
```
We can see that the vector store : vs2 is the IndexFlat of swigfaiss.py, not swigfaiss_avx2.py
I don't know whether the save process is problem, or the load process is problem, but I think it's quite a big bug because two `search` signatures are totally different.
```py
import inspect
inspect.signature(vs.index.search)
# <Signature (x, k, *, params=None, D=None, I=None)>
inspect.signature(vs2.index.search)
# <Signautre (n, x, k, distances, labels, params=None)>
```
Besides, I found the `FAISS_NO_AVX2` is not work, too because when using the flag,
the `replacement_search` is not wrapped the original `search` method at all.
like explained in https://github.com/langchain-ai/langchain/issues/8857
So, how can I use the local save / load mechanism in amd64 arch?
(arm doesn't have any problem)
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
You can refer to my issue above.
### Expected behavior
Can use save/load faiss vectorstore machanism with avx2 IndexFlat in amd64 so that method `search` can wrapped properly by `replacement_search`
|
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| 1,989,888,375 |
PR_kwDOIPDwls5fQkn1
| 13,274 |
update pdf document loaders' metadata source to url for online pdf
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[] | null | 2 | 2023-11-13T04:52:12 | 2023-12-01T23:27:37 | 2023-11-29T20:07:46 |
CONTRIBUTOR
| null |
- **Description:** Update 5 pdf document loaders in `langchain.document_loaders.pdf`, to store a url in the metadata (instead of a temporary, local file path) if the user provides a web path to a pdf: `PyPDFium2Loader`, `PDFMinerLoader`, `PDFMinerPDFasHTMLLoader`, `PyMuPDFLoader`, and `PDFPlumberLoader` were updated.
- The updates follow the approach used to update `PyPDFLoader` for the same behavior in #12092
- The `PyMuPDFLoader` changes required additional work in updating `langchain.document_loaders.parsers.pdf.PyMuPDFParser` to be able to process either an `io.BufferedReader` (from local pdf) or `io.BytesIO` (from online pdf)
- The `PDFMinerPDFasHTMLLoader` change used a simpler approach since the metadata is assigned by the loader and not the parser
- **Issue:** Fixes #7034
- **Dependencies:** None
```python
# PyPDFium2Loader example:
# old behavior
>>> from langchain.document_loaders import PyPDFium2Loader
>>> loader = PyPDFium2Loader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': '/var/folders/7z/d5dt407n673drh1f5cm8spj40000gn/T/tmpm5oqa92f/tmp.pdf', 'page': 0}
# new behavior
>>> from langchain.document_loaders import PyPDFium2Loader
>>> loader = PyPDFium2Loader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': 'https://arxiv.org/pdf/1706.03762.pdf', 'page': 0}
```
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I_kwDOIPDwls52mtIq
| 13,273 |
AWS LangChain using bedrock: Setting Relevance Score Function
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[] | null | 17 | 2023-11-13T04:15:41 | 2023-12-10T23:00:09 | null |
NONE
| null |
### System Info
Thank you for your great help. I have issue with setting the score function (I have AWS LangChain using bedrock). What I have is:
from langchain.vectorstores import FAISS
loader = CSVLoader("./rag_data/a.csv")
documents_aws = loader.load()
docs = CharacterTextSplitter(chunk_size=2000, chunk_overlap=400, separator=",").split_documents(documents_aws)
def custom_score(i):
#return 1 - 1 / (1 + np.exp(i))
return 1
vectorstore_faiss_aws = FAISS.from_documents(documents=docs,embedding = br_embeddings)
vectorstore_faiss_aws.relevance_score_function=custom_score
This made no difference in the scores (did not give any errors either as I am getting large negative numbers and the vectorstore_faiss_aws.similarity_search_with_relevance_scores is indifferent to the score_threshold value)
Then I tried:
vectorstore_faiss_aws = FAISS(relevance_score_fn=custom_score).from_documents(documents=docs,embedding = br_embeddings)
and it gave the following error:
FAISS.__init__() missing 4 required positional arguments: 'embedding_function', 'index', 'docstore', and 'index_to_docstore_id'
Your advice is highly appreciated
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] 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 code is included:
from langchain.vectorstores import FAISS
loader = CSVLoader("./rag_data/a.csv")
documents_aws = loader.load()
docs = CharacterTextSplitter(chunk_size=2000, chunk_overlap=400, separator=",").split_documents(documents_aws)
def custom_score(i):
#return 1 - 1 / (1 + np.exp(i))
return 1
vectorstore_faiss_aws = FAISS.from_documents(documents=docs,embedding = br_embeddings)
vectorstore_faiss_aws.relevance_score_function=custom_score
### Expected behavior
The score to be in the range of 0 and 1 and the vectorstore_faiss_aws.similarity_search_with_relevance_scores to react to score_threshold
|
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PR_kwDOIPDwls5fQS1b
| 13,272 |
Cookbook for multi-modal RAG eval
|
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[] | null | 1 | 2023-11-13T03:12:09 | 2023-11-13T22:26:03 | 2023-11-13T22:26:02 |
COLLABORATOR
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I_kwDOIPDwls52md69
| 13,271 |
https://python.langchain.com/docs/modules/model_io/models/llms/integrations/sagemaker
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[] | null | 4 | 2023-11-13T02:59:08 | 2023-11-13T03:45:21 | null |
NONE
| null |
### Issue with current documentation:
I am try to use sagemaker endpoint to use the embedding model, but I am confused about using EmbeddingsContentHandler, after trying I figure out how to define the transform_input function, but the transform_output did not work as expected, it give out some errors, which like
**embeddings = response_json[0]["embedding"]
TypeError: list indices must be integers or slices, not str**, I would be really garteful it someone konw how to sove it
my ContentHandler function is :
class ContentHandler(EmbeddingsContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs={}) -> bytes:
input_str = json.dumps({"inputs": prompt, **model_kwargs})
return input_str.encode("utf-8")
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
embeddings = response_json[0]["embedding"]
return embeddings
### Idea or request for content:
_No response_
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Chain/Agent Builder
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[] | null | 1 | 2023-11-13T01:44:32 | 2023-11-13T01:44:52 | null |
COLLABORATOR
| null |
### Feature request
The LangChail modules can be naturally used to build something like OpenAI GPTs builder.
My understanding is, LangChain needs to add descriptions and description_embeddings to all integrations/chains/agents (not only to the tools). It allows to build the Super Agent aka Agent Builder.
### Motivation
LangChain's space (in terms of integrations/chains/agents) is bigger than OpenAI's. Let's use this.
### Your contribution
I can help with documentations, examples,ut-s
|
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| 1,989,724,142 |
PR_kwDOIPDwls5fQBKV
| 13,269 |
Support Vald secure connection
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[] | null | 2 | 2023-11-13T01:32:29 | 2023-11-30T05:03:05 | 2023-11-30T03:07:29 |
CONTRIBUTOR
| null |
**Description:**
When using Vald, only insecure grpc connection was supported, so secure connection is now supported.
In addition, grpc metadata can be added to Vald requests to enable authentication with a token.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls52l1oO
| 13,268 |
Issue: gpt3.5-turbo-1106 responds differently from the OpenAI playground
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[] | null | 5 | 2023-11-12T23:20:38 | 2023-11-13T13:20:09 | null |
NONE
| null |
### Issue you'd like to raise.
- Langchain version: 0.0.335
- Affected LLM: chatgpt 3.5 turbo 1106 (NOT affecting: gpt 3.5 turbo 16k, gpt4, gpt4 turbo preview)
- Code template, similar to:
`llm=ChatOpenAI(model_name='gpt-3.5-turbo-1106',
openai_api_key=openai_key
)`
`memory = ConversationBufferMemory()`
`conversation = ConversationChain(
llm=llm, memory=memory
)`
`conversation.predict(input='my instructions')
`
**The problem**
When the identical instructions and parameters are used in the API and the open ai playground, model responds radically different. The open ai playground responds as expected, producing answers. While the API through langchain always returns 'I cannot fufil your request'.
**suspected causes**
Open AI made an upgrade to its API to 1.x, and as far as I know Langchain now only works with open ai 0.28.1. Could that be the reason?
Thanks
**UPDATE 13 Nov:**
I further tested my prompts with OpenAI api 1.2.3 alone, without langchain and everything works fine:
- First I put my prompts into the OpenAI playground, which produced responses
- Second I 'view source' > 'copy', put the python code into a .py file, edited out the last AI response and ran the code = I got the response I wanted.
- Third I tested the same prompt with langchain 0.0.335 (openai 0.28.1) this time, and I always get 'I cannot fulfil this request'.
I know that langchain adds a little bit of context to every request and my prompts did consist of two rounds of chats and thus my code uses memory. I don't know which part of this is problematic.
Again, this problem does not occur with gpt-3.5-turbo, or gpt-4, or gpt-4-turbo, only **'gpt-3.5-turbo-1106'**
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5fPjaR
| 13,267 |
Pgvector template
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[] | null | 4 | 2023-11-12T21:33:45 | 2023-11-14T15:47:49 | 2023-11-14T15:47:48 |
CONTRIBUTOR
| null |
Including pvector template, adapting what is covered in the [cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/retrieval_in_sql.ipynb).
|
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PR_kwDOIPDwls5fPikH
| 13,266 |
Fix serialization issue in Matching Engine Vector Store
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[] | null | 1 | 2023-11-12T21:20:07 | 2023-11-13T19:04:12 | 2023-11-13T19:04:12 |
CONTRIBUTOR
| null |
- **Description:** Fixed a serialization issue in the add_texts method of the Matching Engine Vector Store caused by a typo, leading to an attempt to serialize the json module itself.
- **Issue:** #12154
- **Dependencies:** ./.
- **Tag maintainer:**
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PR_kwDOIPDwls5fPhnh
| 13,265 |
Updated DocugamiLoader with better support for hierarchical chunks
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[] | null | 1 | 2023-11-12T21:05:50 | 2023-11-28T23:56:22 | 2023-11-28T23:56:22 |
CONTRIBUTOR
| null |
There are the following main changes in this PR:
1. Rewrite of the DocugamiLoader to not do any XML parsing of the DGML format internally, and instead use the dgml-utils library we are separately working on. This is a very lightweight dependency.
2. Added MMR search type as an option to multi-vector retriever, similar to other retrievers. MMR is especially useful when using Docugami for RAG since we deal with large sets of documents within which a few might be duplicates and straight similarity based search doesn't give great results in many cases.
We are @docugami on twitter, and I am @tjaffri
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PR_kwDOIPDwls5fPfut
| 13,264 |
Mask PipelineAI API Key
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[] | null | 1 | 2023-11-12T20:47:21 | 2023-11-29T19:57:18 | null |
NONE
| null |
**Description:** Add masking of the API key for Mystic AI's PipelineAI when printed.
**Issue:** #12165
**Dependencies:** None
**Tag maintainer:** @eyurtsev
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I_kwDOIPDwls52lhIO
| 13,263 |
Import error in lanchain/vectorstores/azuresearch.py
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[] | null | 2 | 2023-11-12T20:01:20 | 2023-11-12T20:06:24 | null |
NONE
| null |
I am testing a simple RAG implementation with Azure Cognitive Search. I am seeing a "cannot import name 'Vector' from azure.search.documents.models" error when I invoke my chain. Origin of my error is line 434 in lanchain/vectorstores/azuresearch.py (from azure.search.documents.models import Vector)
this is the relevant code snippet, I get the import error when I execute rag_chain.invoke(question)
from langchain.schema.runnable import RunnablePassthrough
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models.azure_openai import AzureChatOpenAI
question = "my question.."
-- vector_store is initialized using AzureSearch(), not including that snippet here --
retriever = vector_store.as_retriever()
template = '''
Answer the question based on the following context:
{context}
Question: {question}
'''
prompt = ChatPromptTemplate.from_template(template=template)
llm = AzureChatOpenAI( deployment_name='MY_DEPLOYMENT_NAME', model_name='MY_MODEL', openai_api_base=MY_AZURE_OPENAI_ENDPOINT, openai_api_key=MY_AZURE_OPENAI_KEY, openai_api_version='2023-05-15', openai_api_type='azure' )
rag_chain = {'context' : retriever, 'question' : RunnablePassthrough} | prompt | llm
rag_chain.invoke(question)
--------------
my package versions
langchain==0.0.331
azure-search-documents==11.4.0b11
azure-core==1.29.5
openai==0.28.1
_Originally posted by @yallapragada in https://github.com/langchain-ai/langchain/discussions/13245_
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PR_kwDOIPDwls5fPYwG
| 13,262 |
bump openai
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[] | null | 1 | 2023-11-12T19:16:43 | 2023-11-15T00:50:24 | 2023-11-15T00:50:23 |
COLLABORATOR
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PR_kwDOIPDwls5fPYu0
| 13,261 |
bump 335
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PR_kwDOIPDwls5fPXYk
| 13,260 |
Add missing filter to max_marginal_relevance_search inner call to max_marginal_relevance_search_by_vector
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[] | null | 2 | 2023-11-12T18:54:08 | 2023-11-13T18:31:35 | 2023-11-13T18:31:35 |
CONTRIBUTOR
| null |
When calling max_marginal_relevance_search from PGVector the filter param is not carried over to max_marginal_relevance_search_by_vector
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PR_kwDOIPDwls5fPVfT
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Add remove_comments option (default True): do not extract html comments
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[] | null | 1 | 2023-11-12T18:22:31 | 2023-11-29T19:59:40 | null |
CONTRIBUTOR
| null |
- **Description:** add `remove_comments` option (default: True): do not extract html _comments_,
- **Issue:** None,
- **Dependencies:** None,
- **Tag maintainer:** @nfcampos ,
- **Twitter handle:** peter_v
I ran `make format`, `make lint` and `make test`.
Discussion: I my use case, I prefer to not have the comments in the extracted text:
* e.g. from a Google tag that is added in the html as comment
* e.g. content that the authors have temporarily hidden to make it non visible to the regular reader
Removing the comments makes the extracted text more alike the intended text to be seen by the reader.
**Choice to make:** do we prefer to make the default for this `remove_comments` option to be True or False?
I have changed it to True in a second commit, since that is how I would prefer to use it by default. Have the
cleaned text (without technical Google tags etc.) and also closer to the actually visible and intended content.
I am not sure what is best aligned with the conventions of langchain in general ...
INITIAL VERSION (new version above):
~**Choice to make:** do we prefer to make the default for this `ignore_comments` option to be True or False?
I have set it to False now to be backwards compatible. On the other hand, I would use it mostly with True.
I am not sure what is best aligned with the conventions of langchain in general ...~
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PR_kwDOIPDwls5fPRoE
| 13,257 |
Added support for a Pandas DataFrame OutputParser
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[] | null | 4 | 2023-11-12T17:20:17 | 2023-11-30T03:09:34 | 2023-11-30T03:08:50 |
CONTRIBUTOR
| null |
**Description:**
Added support for a Pandas DataFrame OutputParser with format instructions, along with unit tests and a demo notebook. Namely, we've added the ability to request data from a DataFrame, have the LLM parse the request, and then use that request to retrieve a well-formatted response.
Within LangChain, it seamlessly integrates with language models like OpenAI's `text-davinci-003`, facilitating streamlined interaction using the format instructions (just like the other output parsers).
This parser structures its requests as `<operation/column/row>[<optional_array_params>]`. The instructions detail permissible operations, valid columns, and array formats, ensuring clarity and adherence to the required format.
For example:
- When the LLM receives the input: "Retrieve the mean of `num_legs` from rows 1 to 3."
- The provided format instructions guide the LLM to structure the request as: "mean:num_legs[1..3]".
The parser processes this formatted request, leveraging the LLM's understanding to extract the mean of `num_legs` from rows 1 to 3 within the Pandas DataFrame.
This integration allows users to communicate requests naturally, with the LLM transforming these instructions into structured commands understood by the `PandasDataFrameOutputParser`. The format instructions act as a bridge between natural language queries and precise DataFrame operations, optimizing communication and data retrieval.
**Issue:**
- https://github.com/langchain-ai/langchain/issues/11532
**Dependencies:**
No additional dependencies :)
**Tag maintainer:**
@baskaryan
**Twitter handle:**
No need. :)
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I_kwDOIPDwls52lUVw
| 13,256 |
ImportError: cannot import name 'BabyAGI' from 'langchain_experimental'
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[] | null | 2 | 2023-11-12T17:16:05 | 2023-11-12T17:24:02 | null |
NONE
| null |
### System Info
I can not import BabyAGI
from langchain_experimental import BabyAGI
The langchain version is:
pip show langchain
Name: langchain
Version: 0.0.334
Summary: Building applications with LLMs through composability
Home-page: https://github.com/langchain-ai/langchain
Author:
Author-email:
License: MIT
Location: c:\users\test\anaconda3\envs\nenv\lib\site-packages
Requires: aiohttp, anyio, async-timeout, dataclasses-json, jsonpatch, langsmith, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity
Required-by: langchain-experimental
### Who can help?
_No response_
### 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
import dotenv
from dotenv import load_dotenv
load_dotenv()
import os
import langchain
from langchain.embeddings import OpenAIEmbeddings
import faiss
from langchain.vectorstores import FAISS
from langchain.docstore import InMemoryDocstore
# Define the embedding model
embeddings_model = OpenAIEmbeddings(model="text-embedding-ada-002")
# Initialize the vectorstore
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model, index, InMemoryDocstore({}), {})
from langchain.llms import OpenAI
from langchain_experimental import BabyAGI
### Expected behavior
I want to be able to execute:
# Define the embedding model
embeddings_model = OpenAIEmbeddings(model="text-embedding-ada-002")
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OpenAI Assistant in Python
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[] | null | 1 | 2023-11-12T14:58:55 | 2023-11-26T14:48:34 | 2023-11-26T14:48:34 |
CONTRIBUTOR
| null |
### Feature request
I hope this message finds you well. I noticed that OpenAI has recently unveiled their Assistant API, and I observed that the Langchain framework in JavaScript has already implemented an agent utilizing this new API. Could you kindly provide information on when this integration will be extended to Python?
For your reference, here is the documentation for the JavaScript implementation: [JavaScript Doc Reference](https://js.langchain.com/docs/modules/agents/agent_types/openai_assistant).
Thank you for your assistance.
### Motivation
This is a new feature from OpenAI and will be really useful for building LLM-based Apps which needs to utilize code generation feature.
### Your contribution
N/A
|
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PR_kwDOIPDwls5fO7Da
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Add `SagemakerAsyncEndpoint` to use sagemaker async endpoints
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[] | null | 3 | 2023-11-12T12:38:49 | 2023-11-25T14:13:13 | null |
NONE
| null |
- **Description:** Add a SagemakerAsyncEndpoint to be able to use not only realtime inference Endpoints. Started from #6940 and added some enhancements. Moreover, fixed documentation issues for SagemakerEndpoint as well.
- **Issue:** does not apply
- **Dependencies:** no additional dependencies
- **Tag maintainer:**
- **Twitter handle:** small and no thanks :-)
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PR_kwDOIPDwls5fOtGx
| 13,253 |
Add a `MultiFieldRetriever` to search content and metadata at the sametime.
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[] | null | 6 | 2023-11-12T09:05:52 | 2023-12-09T13:37:31 | null |
CONTRIBUTOR
| null |
- **Description:** I created the `MultiFieldRetriever` to search not only the content of a document but also its metadata. The difference between it and the `Self-query Retriever` is that the `MultiFieldRetriever` merges the document's content and metadata and performs a single search, rather than searching the content first and then filtering the results by metadata. **You can see how it works in the [document notebook ](https://github.com/langchain-ai/langchain/blob/008618d38a3f8329c5aef4f2eb1f5da4f1e89401/docs/docs/modules/data_connection/retrievers/multi_field.ipynb)
- **Dependencies:** No
- **Tag maintainer:** @baskaryan
- **Twitter handle:** DayuanJian21687
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PR_kwDOIPDwls5fOjwO
| 13,252 |
docs: align custom_tool document headers
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[] | null | 1 | 2023-11-12T07:07:51 | 2023-11-13T17:03:56 | 2023-11-13T17:03:56 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
On the [Defining Custom Tools](https://python.langchain.com/docs/modules/agents/tools/custom_tools) page, there's a 'Subclassing the BaseTool class' paragraph under the 'Completely New Tools - String Input and Output' header. Also there's another 'Subclassing the BaseTool' paragraph under no header, which I think may belong to the 'Custom Structured Tools' header.
Another thing is, there's a 'Using the tool decorator' and a 'Using the decorator' paragraph, I think should belong to 'Completely New Tools - String Input and Output' and 'Custom Structured Tools' separately.
This PR moves those paragraphs to corresponding headers.
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I_kwDOIPDwls52kb02
| 13,251 |
Any time I try to use a openAPI API that requires auth, the openapi_chain call fails.
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[] | null | 10 | 2023-11-12T06:43:23 | 2023-11-15T13:50:15 | null |
NONE
| null |
### System Info
langchain 0.0.334
python 3.11.5
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
hubspot_chain = get_openapi_chain("https://api.hubspot.com/api-catalog-public/v1/apis/crm/v3/objects/companies",
headers={"Authorization": "Bearer <My Hubspot API token>"})
hubspot_chain("Fetch all contacts in my CRM, please")
I get the error :
BadRequestError: Error code: 400 - {'error': {'message': "'get__crm_v3_objects_companies_{companyId}_getById' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'functions.1.name'", 'type': 'invalid_request_error', 'param': None, 'code': None}}
This happens with other APIs too, I've tried the following :
1. OpenAPI
2. Asana
3. Hubspot
4. Slack
None work.
### Expected behavior
The API calls should work, I should get response to my natural language requests, which should get internally mapped to actions, as specified in the OpenAPI specs I've supplied.
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I_kwDOIPDwls52kbBC
| 13,250 |
Issue: How to use other natural languages besides English?
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[] | null | 4 | 2023-11-12T06:28:49 | 2023-11-13T22:48:42 | null |
NONE
| null |
### Issue you'd like to raise.
Hello,
Thanks for this framework. It's making everyone's work simpler!
I'm using an LLM model to infer data from Portuguese websites, and expecting answers in Portuguese. But some of Langchain features, namely the "Get format instructions" for Output Parses come written in English. I'm not sure what the best approach would be here.
Should I do all my prompting in English, and just add "Answer in Portuguese" at the end?
Should this be a feature in Langchain, and if so, how would it work?
I'm not sure asking the community to translate it would be the right approach, because this is not just about translation, but making sure the prompts work correctly. I would be fine with some api call to replace the English text with my translation, but that doesn't seem to be part of the public API at the moment.
Thanks,
### Suggestion:
_No response_
|
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| 1,989,251,097 |
I_kwDOIPDwls52kZAZ
| 13,249 |
why when the system doesn't find the answer to the user's question, show hadith still appears?
|
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[] | null | 21 | 2023-11-12T05:49:18 | 2023-11-12T08:59:32 | null |
NONE
| null |
async def query(update: Update, context: CallbackContext):
global chain, metadatas, texts
if chain is None:
await context.bot.send_message(
chat_id=update.effective_chat.id,
text="Please load the chain first using /load")
return
user_query = update.message.text
cb = AsyncFinalIteratorCallbackHandler()
cb.stream_final_answer = True
cb.answer_prefix_tokens = ["FINAL", "ANSWER"]
cb.answer_reached = True
res = await chain.acall(user_query, callbacks=[cb])
answer = res["answer"]
sources = res.get("source_documents", [])
context.user_data['sources'] = sources
await context.bot.send_message(chat_id=update.effective_chat.id, text=answer)
for idx, source in enumerate(sources, start=1):
source_name = source.metadata.get("source", f"Unknown Source {idx}").replace(".", "")
keyboard = [[InlineKeyboardButton("Show Hadith", callback_data=str(idx))]]
await context.bot.send_message(chat_id=update.effective_chat.id,
text=f"{idx}. {source_name}",
reply_markup=InlineKeyboardMarkup(keyboard))
### Idea or request for content:
please help me, if the system cannot find the user's answer based on the existing context then the show hadith will not be displayed either the source name or keyboard
|
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PR_kwDOIPDwls5fOfbt
| 13,248 |
Add docugami-kg-rag template
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CONTRIBUTOR
| null |
**Description**: Adds a new template that uses the Docugami Loader for KG-RAG conversation. This template contains a reference architecture for Retrieval Augmented Generation against a set of documents using Docugami's XML Knowledge Graph (KG-RAG).
More information found in the [README](https://github.com/docugami/langchain/tree/kgrag/template/templates/docugami-kg-rag#readme)
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| 1,989,233,891 |
I_kwDOIPDwls52kUzj
| 13,247 |
Issue: <openai APIConnectionError>
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[] | null | 3 | 2023-11-12T04:30:51 | 2023-11-12T05:09:02 | null |
NONE
| null |
### Issue you'd like to raise.
```
import uvicorn
import os
from typing import AsyncIterable, Awaitable
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import FileResponse, StreamingResponse
from langchain.callbacks import AsyncIteratorCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, ChatMessage
import asyncio
async def wait_done(fn, event):
try:
await fn
except Exception as e:
print('error', e)
event.set()
finally:
event.set()
async def call_openai(question):
callback = AsyncIteratorCallbackHandler()
model = ChatOpenAI(
openai_api_key= os.environ["OPENAI_API_KEY"],
streaming=True,
callbacks=[callback]
)
print('question', question)
coroutine = wait_done(model.agenerate(messages=[[HumanMessage(content=question)]]), callback.done)
task = asyncio.create_task(coroutine)
print('task', task)
print('coroutine', callback.aiter())
async for token in callback.aiter():
yield f"{token}"
await task
app = FastAPI()
@app.get("/")
async def homepage():
return FileResponse('static/index.html')
@app.post("/ask")
def ask(body: dict):
print('body', body)
# return call_openai(body['question'])
return StreamingResponse(call_openai(body['question']), media_type="text/event-stream")
if __name__ == "__main__":
uvicorn.run(host="127.0.0.1", port=8888, app=app)
```
run it:
(venv) (base) zhanglei@zhangleideMacBook-Pro chatbot % python server.py
INFO: Started server process [46402]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:8888 (Press CTRL+C to quit)
INFO: 127.0.0.1:53273 - "GET / HTTP/1.1" 200 OK
body {'question': '你好'}
INFO: 127.0.0.1:53273 - "POST /ask HTTP/1.1" 200 OK
question 你好
question 你好
task <Task pending name='Task-6' coro=<wait_done() running at /Users/zhanglei/Desktop/github/chatbot/server.py:21>>
coroutine <async_generator object AsyncIteratorCallbackHandler.aiter at 0x117158e40>
Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
### Suggestion:
_No response_
|
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I_kwDOIPDwls52kMB7
| 13,246 |
when i use map_reduce type, error appear
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[] | null | 2 | 2023-11-12T02:00:07 | 2023-11-12T02:06:38 | null |
NONE
| null |
### Issue you'd like to raise.
when chain_type='stuff', normal
when
chain_type = 'map_reduce', error:
1 validation error for RetrievalQA
question_prompt
extra fields not permitted (type=value_error.extra)
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5fOMdU
| 13,244 |
Add new models to openai callback
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[] | null | 2 | 2023-11-11T23:08:55 | 2023-11-12T20:01:20 | 2023-11-12T20:01:20 |
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!
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**Description:** Adding the new models to the openai callback function, info taken from [model announcement](https://platform.openai.com/docs/models) and [pricing](https://openai.com/pricing)
A short description for a short PR :)
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PR_kwDOIPDwls5fOI0F
| 13,243 |
update tools
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| 1,989,042,216 |
I_kwDOIPDwls52jmAo
| 13,242 |
I get a `NotImplementedError` when I use `docsearch.as_retriever` with `similarity_score_threshold`
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[] | null | 5 | 2023-11-11T17:04:13 | 2023-11-13T13:05:12 | null |
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| null |
### Discussed in https://github.com/langchain-ai/langchain/discussions/12799
<div type='discussions-op-text'>
<sup>Originally posted by **younes-io** November 2, 2023</sup>
I have an `NotImplementedError: ` error when I run this code:
```python
embeddings = OpenAIEmbeddings(deployment=embedding_model, chunk_size=1)
docsearch = OpenSearchVectorSearch(index_name=index_company_docs, embedding_function=embeddings,opensearch_url=opensearch_url, http_auth=('user', auth))
doc_retriever = docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={'score_threshold': 0.8}
)
qa = RetrievalQAWithSourcesChain.from_chain_type(
memory=memory,
llm=llm,
chain_type="stuff", # See other types of chains here
retriever=doc_retriever,
return_source_documents=True,
verbose=True,
chain_type_kwargs=chain_type_kwargs,
)
response = qa({"question": "When was the company founded?"})
```</div>
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I_kwDOIPDwls52jFGn
| 13,241 |
@tool decorator for StructuredTool.from_function doesn't fill in the `__name__` attribute correctly
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[] | null | 3 | 2023-11-11T11:37:37 | 2023-11-11T12:14:23 | 2023-11-11T12:14:22 |
NONE
| null |
### System Info
Macos: 13.4.1 (apple silicon M1)
python version 3.10.13
relevant packages:
langchain 0.0.334
pydantic 1.10.13
pydantic_core 2.10.1
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://colab.research.google.com/drive/18d2UIFd3LHwkD3ml6_OydetvytKNnoBW?usp=sharing
1. `from langchain.tools import tool`
2. define a simple structured function
3. Use `StructuredTool.from_function(simple_function)`
### Expected behavior
I expected that it would either work, or tell me that the @tool decorator is not meant to be used with StructuredTool. This is not specified anywhere in the docs or code.
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