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| 1,861,049,462 |
I_kwDOIPDwls5u7Vx2
| 9,593 |
Allow specifying dtype in `langchain.llms.VLLM`
|
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[] | null | 1 | 2023-08-22T09:33:12 | 2023-08-23T03:30:28 | 2023-08-23T03:30:28 |
CONTRIBUTOR
| null |
### Feature request
Add option to specify dtype (e.g. `float16`) in `langchain.llms.VLLM()` constructor,
### Motivation
Currently, `vllm` library provides an argument to use `float16` dtype, but langchain doesn't.
There is no way to use langchain VLLM with a GPU < 8.0 compute capability.
### Your contribution
I can contribute with guideline from langchain team
|
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PR_kwDOIPDwls5Yd0bN
| 9,589 |
Merge from original langchain
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NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
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2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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|
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| 1,860,810,521 |
PR_kwDOIPDwls5YdrYr
| 9,588 |
Replacing Exception type from ValueError to ImportError
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[] | null | 2 | 2023-08-22T07:40:55 | 2023-08-22T14:34:06 | 2023-08-22T14:34:06 |
CONTRIBUTOR
| null |
I have restructured the code to ensure uniform handling of ImportError. In place of previously used ValueError, I've adopted the standard practice of raising ImportError with explanatory messages. This modification enhances code readability and clarifies that any problems stem from module importation.
@eyurtsev , @baskaryan
Thanks
|
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| 1,860,801,274 |
I_kwDOIPDwls5u6ZL6
| 9,587 |
Regression: Additional arguments in TransformChain subclass no longer work
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[] | null | 2 | 2023-08-22T07:34:56 | 2023-11-28T16:07:50 | 2023-11-28T16:07:49 |
NONE
| null |
### System Info
LangChain Version: 0.0.260
Python Version: 3.11.4
Operating System: ubuntu-22.04
### 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
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
After updating from version 0.0.256 to 0.0.260, I noticed that the TransformChain subclass which used to accept additional arguments (memory) no longer works. This regression might be related to changes made in [Pull Request #8762](https://github.com/langchain-ai/langchain/pull/8762).
### Relevant Code:
Here's a snippet of the MySubclassedTransformChain:
```python
class MySubclassedTransformChain(TransformChain):
memory: BaseChatMemory
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
return self.transform(inputs, memory=self.memory)
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
if self.atransform is not None:
return await self.atransform(inputs, memory=self.memory)
else:
self._log_once(
"TransformChain's atransform is not provided, falling"
" back to synchronous transform"
)
return self.transform(inputs, memory=self.memory)
```
Usage:
```
transform_chain = MySubclassedTransformChain(input_variables=["input"], output_variables=["output"], transform=transform_func, memory=memory)
llm_chain = LLMChain(llm=llm, prompt=prompt, memory=memory)
sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])
sequential_chain.run(message)
```
Exception Stack Trace:
```
app-1 | ERROR: Exception in ASGI application
app-1 | Traceback (most recent call last):
app-1 | File "/usr/local/lib/python3.11/site-packages/uvicorn/protocols/http/h11_impl.py", line 408, in run_asgi
app-1 | result = await app( # type: ignore[func-returns-value]
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 84, in __call__
app-1 | return await self.app(scope, receive, send)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/fastapi/applications.py", line 289, in __call__
app-1 | await super().__call__(scope, receive, send)
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 348, in _sentry_patched_asgi_app
app-1 | return await middleware(scope, receive, send)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/asgi.py", line 141, in _run_asgi3
app-1 | return await self._run_app(scope, lambda: self.app(scope, receive, send))
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/asgi.py", line 190, in _run_app
app-1 | raise exc from None
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/asgi.py", line 185, in _run_app
app-1 | return await callback()
app-1 | ^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/starlette/applications.py", line 122, in __call__
app-1 | await self.middleware_stack(scope, receive, send)
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 143, in _create_span_call
app-1 | return await old_call(app, scope, new_receive, new_send, **kwargs)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 184, in __call__
app-1 | raise exc
app-1 | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 162, in __call__
app-1 | await self.app(scope, receive, _send)
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 242, in _sentry_exceptionmiddleware_call
app-1 | await old_call(self, scope, receive, send)
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 143, in _create_span_call
app-1 | return await old_call(app, scope, new_receive, new_send, **kwargs)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 79, in __call__
app-1 | raise exc
app-1 | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 68, in __call__
app-1 | await self.app(scope, receive, sender)
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 143, in _create_span_call
app-1 | return await old_call(app, scope, new_receive, new_send, **kwargs)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/fastapi/middleware/asyncexitstack.py", line 20, in __call__
app-1 | raise e
app-1 | File "/usr/local/lib/python3.11/site-packages/fastapi/middleware/asyncexitstack.py", line 17, in __call__
app-1 | await self.app(scope, receive, send)
app-1 | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 718, in __call__
app-1 | await route.handle(scope, receive, send)
app-1 | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 276, in handle
app-1 | await self.app(scope, receive, send)
app-1 | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 66, in app
app-1 | response = await func(request)
app-1 | ^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/sentry_sdk/integrations/fastapi.py", line 131, in _sentry_app
app-1 | return await old_app(*args, **kwargs)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/fastapi/routing.py", line 273, in app
app-1 | raw_response = await run_endpoint_function(
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/fastapi/routing.py", line 190, in run_endpoint_function
app-1 | return await dependant.call(**values)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/app/app/main.py", line 47, in post_message
app-1 | result = await handle_message(message)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/app/app/message_chain.py", line 20, in handle_message
app-1 | assistant_result = assistant_chain.run(extraction_result, memory=manager.memory)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/app/app/assistant_chain.py", line 63, in run
app-1 | response = sequential_chain.run(message)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/langchain/chains/base.py", line 475, in run
app-1 | return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/langchain/chains/base.py", line 282, in __call__
app-1 | raise e
app-1 | File "/usr/local/lib/python3.11/site-packages/langchain/chains/base.py", line 276, in __call__
app-1 | self._call(inputs, run_manager=run_manager)
app-1 | File "/usr/local/lib/python3.11/site-packages/langchain/chains/sequential.py", line 180, in _call
app-1 | _input = chain.run(_input, callbacks=_run_manager.get_child(f"step_{i+1}"))
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/langchain/chains/base.py", line 475, in run
app-1 | return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | File "/usr/local/lib/python3.11/site-packages/langchain/chains/base.py", line 282, in __call__
app-1 | raise e
app-1 | File "/usr/local/lib/python3.11/site-packages/langchain/chains/base.py", line 276, in __call__
app-1 | self._call(inputs, run_manager=run_manager)
app-1 | File "/app/app/langchain_extensions.py", line 14, in _call
app-1 | return self.transform(inputs, memory=self.memory)
app-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
app-1 | TypeError: Runnable.transform() got an unexpected keyword argument 'memory'
```
Transform Function:
```
def transform_func(inputs: dict, memory: BaseChatMemory) -> dict:
text = inputs["text"]
# mutate text with memory
return {"output": text}
```
### Expected behavior
The subclass should handle additional arguments in MySubclassedTransformChain as it did in version 0.0.256.
|
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PR_kwDOIPDwls5Ydoi6
| 9,586 |
Update the nlpcloud connector after some changes on the NLP Cloud API
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CONTRIBUTOR
| null |
- Description: remove some text generation deprecated parameters and update the embeddings doc,
- Tag maintainer: @rlancemartin
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I_kwDOIPDwls5u6VCe
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Direct initial redis database can't successfully use searching function since index missing
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[] | null | 5 | 2023-08-22T07:24:40 | 2023-10-17T22:23:20 | null |
NONE
| null |
### System Info
langchain version: 0.0.267
### Who can help?
@hwchase17
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# Redis vector database initialization
<p>
embeddings = OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_TYPE"), <br/>
deployment=os.getenv("OPENAI_EMBEDDING_MODEL_NAME"), <br/>
client="azure", <br/>
chunk_size=1)<br/>
redis_vector_db = Redis(<br/>
redis_url="redis://localhost:6379", <br/>
index_name="test_1", <br/>
embedding_function=embedding.embed_query<br/>
)
</p>
# Load pdf
loader = PyPDFLoader( file_path)
docs= loader.load_and_split()
redis_vector_db.add_documents(docs, index_name = "test_1")
# Searching --> bug here
docs = redis_vector_db.similarity_search(query = question,k = 4)
-------------------------- This is the end of python script ---------------------------------------
### Expected behavior
# Bug: Even though we have set the index name it will have bug of no such index
File [c:\Users\User\anaconda3\envs\advantech\lib\site-packages\langchain\vectorstores\redis.py:284](file:///C:/Users/User/anaconda3/envs/advantech/lib/site-packages/langchain/vectorstores/redis.py:284), in Redis.similarity_search(self, query, k, **kwargs)
271 def similarity_search(
272 self, query: str, k: int = 4, **kwargs: Any
273 ) -> List[Document]:
274 """
275 Returns the most similar indexed documents to the query text.
276
(...)
282 List[Document]: A list of documents that are most similar to the query text.
283 """
--> 284 docs_and_scores = self.similarity_search_with_score(query, k=k)
285 return [doc for doc, _ in docs_and_scores]
File [c:\Users\User\anaconda3\envs\advantech\lib\site-packages\langchain\vectorstores\redis.py:361](file:///C:/Users/User/anaconda3/envs/advantech/lib/site-packages/langchain/vectorstores/redis.py:361), in Redis.similarity_search_with_score(self, query, k)
354 params_dict: Mapping[str, str] = {
355 "vector": np.array(embedding) # type: ignore
...
904 if isinstance(response, ResponseError):
--> 905 raise response
906 return response
ResponseError: test_1: no such index
# My observation
print(redis_vector_db.index_name)
will show test_1
|
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PR_kwDOIPDwls5YdcC1
| 9,584 |
import tqdm.auto instead of tqdm tqdm for OpenAIEmbeddings
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[] | null | 2 | 2023-08-22T06:59:49 | 2023-08-22T18:54:08 | 2023-08-22T18:54:08 |
CONTRIBUTOR
| null |
- Description: current code does not work very well on jupyter notebook, so I changed the code so that it imports `tqdm.auto` instead.
- Issue: #9582
- Dependencies: N/A
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
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PR_kwDOIPDwls5YdbUY
| 9,583 |
Adds transform and atransform support to runnable sequences
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CONTRIBUTOR
| null |
Allow runnable sequences to support transform if each individual runnable inside supports transform/atransform.
@nfcampos
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I_kwDOIPDwls5u6JOd
| 9,582 |
Replace `tqdm` with `tqdm.auto`
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[] | null | 2 | 2023-08-22T06:56:51 | 2023-08-23T00:41:43 | 2023-08-23T00:41:42 |
CONTRIBUTOR
| null |
### Feature request
```
if self.show_progress_bar:
try:
import tqdm
_iter = tqdm.tqdm(range(0, len(tokens), _chunk_size))
except ImportError:
_iter = range(0, len(tokens), _chunk_size)
```
Current code does not work very well on jupyter notebook, so replace it with `tqdm.auto`
### Motivation
It just does not work very well on jupyter notebook, especially if there's any warning
### Your contribution
I'll make a PR
|
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| 1,860,722,360 |
I_kwDOIPDwls5u6F64
| 9,581 |
make test in experimental crash
|
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[] | null | 2 | 2023-08-22T06:49:07 | 2023-09-19T08:47:29 | 2023-09-19T08:47:29 |
CONTRIBUTOR
| null |
### System Info
langchain version 0.0.270
### Who can help?
@hwchase17
### 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
cd lib/experimental
make test
```
============================================================================= short test summary info =============================================================================
FAILED tests/unit_tests/test_smartllm.py::test_all_steps - IndexError: list index out of range
FAILED tests/unit_tests/test_smartllm.py::test_intermediate_output - IndexError: list index out of range
========== 2 failed, 28 passed, 16 warnings in 4.37s =========
```
### Expected behavior
No error
|
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| 1,860,626,011 |
PR_kwDOIPDwls5YdClc
| 9,579 |
Added a pdf parser based on DocAI
|
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[] | null | 4 | 2023-08-22T05:53:11 | 2023-08-25T04:44:49 | 2023-08-25T04:44:49 |
CONTRIBUTOR
| null |
#9578
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| 1,860,625,662 |
I_kwDOIPDwls5u5uT-
| 9,578 |
Add support for DocAI from Google Cloud as a pdf parser
|
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[] | null | 2 | 2023-08-22T05:52:46 | 2023-11-28T16:07:55 | 2023-11-28T16:07:54 |
CONTRIBUTOR
| null |
### Feature request
Add support for DocAI from Google Cloud as a pdf parser
### Motivation
It might be faster for large samples of documents (and maybe better in some cases).
### Your contribution
yep, I'm happy to
|
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| 1,860,602,217 |
PR_kwDOIPDwls5Yc9U3
| 9,577 |
Hub stub updates
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[] | null | 1 | 2023-08-22T05:34:12 | 2023-08-22T23:05:42 | 2023-08-22T23:05:41 |
COLLABORATOR
| null |
Updates the hub stubs to not fail when no api key is found. For supporting singleton tenants and default values from sdk 0.1.6.
Also adds the ability to define is_public and description for backup repo creation on push.
|
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| 1,860,497,298 |
I_kwDOIPDwls5u5O-S
| 9,576 |
HuggingFaceEndpoint Returns an Empty String
|
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[] | null | 2 | 2023-08-22T03:52:51 | 2023-11-28T16:08:00 | 2023-11-28T16:07:59 |
NONE
| null |
### System Info
HuggingFaceEndpoint Returns an Empty String both when prompted using ._call() method and when used as an LLM in a QA chain. Examples of what I've tried, below:
```
from langchain.llms import HuggingFaceEndpoint
from langchain.chains.question_answering import load_qa_chain
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
### Example 1 -- returns empty string
endpoint_url = (
"my-real-endpoint-here"
)
llm = HuggingFaceEndpoint(
endpoint_url = endpoint_url,
huggingfacehub_api_token = os.environ['HUGGING_FACE_HUB_TOKEN'],
task = 'text2text-generation',
model_kwargs = {'temperature': 1e-20, "max_length": 900},
)
llm._call(prompt = "What is 4 + 4? Think the question through, step by step.")
>>> ""
### Example 2 -- used in QA chain and also returns empty string
### (exact same 'llm' object)
### text splitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 15)
### embeddings
embeddings = HuggingFaceEmbeddings()
chain = load_qa_chain(llm, chain_type = "stuff", verbose = False)
def extract_from_case(file_str: str, prompt_question: str):
docs = text_splitter.split_text(file_str)
db = FAISS.from_texts(docs, embeddings)
docs = db.similarity_search(prompt_question)
extraction = chain.run(input_documents = docs, question = prompt_question)
return extraction
sample_file = """Metadata: Date: 2017-01-18 File number: CEL-62600-16 CEL-62600-16 Citation: CEL-62600-16 (Re), 2017, retrieved on 2023-05-16. Content: Arrears Worksheet File Number: CEL-62600-16 Time period for Arrears Owing From: September 1, 2016 to"""
prompt = "What is the file number of this case?"
extract_from_case(file_str = sample_file, prompt_question = prompt)
>>> ""
```
### Who can help?
@hwchase17 @agola11
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
See above.
### Expected behavior
To return a non-empty response.
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| 1,860,477,232 |
I_kwDOIPDwls5u5KEw
| 9,575 |
Additional/Popular chains Page Not Found
|
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[] | null | 2 | 2023-08-22T03:24:06 | 2023-11-28T16:08:05 | 2023-11-28T16:08:04 |
NONE
| null |
### Issue with current documentation:
I'm getting this:
Page Not Found
We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken.
When visiting:
- https://python.langchain.com/docs/modules/chains/additional/
- https://python.langchain.com/docs/modules/chains/popular/
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5YcT9h
| 9,574 |
bugfix: ArangoDB Empty Schema Case
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[] | null | 2 | 2023-08-22T01:52:11 | 2023-08-22T14:41:07 | 2023-08-22T14:41:06 |
CONTRIBUTOR
| null |
- Introduces a conditional in `ArangoGraph.generate_schema()` to exclude empty ArangoDB Collections from the schema
- Add empty collection test case
Issue: N/A
Dependencies: None
|
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PR_kwDOIPDwls5YcOuw
| 9,573 |
Remove -E all from installation instructions
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[] | null | 4 | 2023-08-22T01:12:29 | 2023-08-22T23:16:27 | 2023-08-22T18:57:58 |
CONTRIBUTOR
| null |
Using poetry install -E all no longer works since [tool.poetry.extras] was removed from pyproject.toml, so I removed it and the blurb explaining it from the installation instructions.
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PR_kwDOIPDwls5Yb8Zh
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rfc: with_fallbacks maintains type
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COLLABORATOR
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PR_kwDOIPDwls5Yb3gR
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Document AzureML Deployment Example
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CONTRIBUTOR
| null |
Description: Link an example of deploying a Langchain app to an AzureML online endpoint to the deployments documentation page.
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PR_kwDOIPDwls5Ybu0Y
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parent retriever nit
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[] | null | 1 | 2023-08-21T22:07:01 | 2023-08-22T18:58:17 | 2023-08-22T18:58:17 |
COLLABORATOR
| null |
if ids are nullable seems like they should have default val None. mirrors VectorStore interface as well. cc @mcantillon21 @jacoblee93
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PR_kwDOIPDwls5Ybpg1
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Add missing param to parent document retriever notebook
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Bagatur/confluence val
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COLLABORATOR
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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/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5Ybi4B
| 9,567 |
Use the GitHub-suggested safer pattern for shell interpolation.
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[] | null | 1 | 2023-08-21T21:31:35 | 2023-08-21T21:59:11 | 2023-08-21T21:59:10 |
COLLABORATOR
| null |
Using `${{ }}` to construct shell commands is risky, since the `${{ }}` interpolation runs first and ignores shell quoting rules. This means that shell commands that look safely quoted, like `echo "${{ github.event.issue.title }}"`, are actually vulnerable to shell injection.
More details here: https://github.blog/2023-08-09-four-tips-to-keep-your-github-actions-workflows-secure/
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PR_kwDOIPDwls5YbgCS
| 9,566 |
Use verbose pipx install to see why it's taking so long.
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[] | null | 2 | 2023-08-21T21:23:13 | 2023-08-22T19:39:59 | 2023-08-22T19:39:55 |
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PR_kwDOIPDwls5YbeOk
| 9,565 |
Fix typo
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[] | null | 2 | 2023-08-21T21:16:20 | 2023-08-21T22:54:39 | 2023-08-21T22:54:39 |
CONTRIBUTOR
| null |
Corrected a minor documentation typo here: https://python.langchain.com/docs/modules/model_io/models/llms/#generate-batch-calls-richer-outputs
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PR_kwDOIPDwls5YbV3y
| 9,564 |
Set up concurrency groups and workflow cancelation in CI.
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[] | null | 1 | 2023-08-21T20:48:37 | 2023-08-22T18:22:19 | 2023-08-22T18:21:26 |
COLLABORATOR
| null |
If another push to the same PR or branch happens while its CI is still running, cancel the earlier run in favor of the next run.
There's no point in testing an outdated version of the code. GitHub only allows a limited number of job runners to be active at the same time, so it's better to cancel pointless jobs early so that more useful jobs can run sooner.
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PR_kwDOIPDwls5YbODb
| 9,563 |
add variables for field names
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[] | null | 1 | 2023-08-21T20:24:26 | 2023-08-22T14:43:22 | 2023-08-22T14:43:21 |
COLLABORATOR
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PR_kwDOIPDwls5YbKP4
| 9,562 |
Eliminate special-casing from test CI workflows.
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[] | null | 3 | 2023-08-21T20:13:07 | 2023-08-22T15:36:53 | 2023-08-22T15:36:52 |
COLLABORATOR
| null |
The previous approach was relying on `_test.yml` taking an input parameter, and then doing almost completely orthogonal things for each parameter value. I've separated out each of those test situations as its own job or workflow file, which eliminated all the special-casing and, in my opinion, improved maintainability by making it much more obvious what code runs when.
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PR_kwDOIPDwls5YbFZT
| 9,561 |
update data connection -> retrieval
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[] | null | 1 | 2023-08-21T20:00:26 | 2023-08-21T20:03:30 | 2023-08-21T20:03:29 |
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I_kwDOIPDwls5u3VFy
| 9,559 |
Adding thumbs-up and thumbs-down for documentations
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[] | null | 1 | 2023-08-21T19:23:20 | 2023-11-27T16:06:16 | 2023-11-27T16:06:16 |
NONE
| null |
### Feature request
It might be very helpful to let users react with each page of the documentation so that contributors, users and people working will know which document has been out-dated / not usable.
### Motivation
I saw many inconsistencies in different docs and some codes are no longer usable as the dependancies update. It would be very help if there is an indicator of whether that documentation is still something we could reply on.
### Your contribution
I could contribute on the front-end but not sure how to configure it on langchain's database.
|
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PR_kwDOIPDwls5YatUR
| 9,558 |
Update `SECURITY.md` email address.
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[] | null | 1 | 2023-08-21T18:45:59 | 2023-08-21T18:55:04 | 2023-08-21T18:52:21 |
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PR_kwDOIPDwls5Yaqlv
| 9,557 |
Add `py.typed` file to `langchain-experimental`.
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[] | null | 2 | 2023-08-21T18:36:12 | 2023-08-21T19:37:17 | 2023-08-21T19:37:16 |
COLLABORATOR
| null |
The package is linted with mypy, so its type hints are correct and should be exposed publicly. Without this file, the type hints remain private and cannot be used by downstream users of the package.
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PR_kwDOIPDwls5Yaqld
| 9,556 |
pin pydantic api ref build
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PR_kwDOIPDwls5Yapzq
| 9,555 |
Add additional_kwargs to BasePromptTemplate, allow use in ChatPromptTemplate
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[] | null | 5 | 2023-08-21T18:33:27 | 2023-09-17T18:43:42 | 2023-09-04T03:50:15 |
CONTRIBUTOR
| null |
It can be useful to annotate templates with 3rd party metadata using `additional_kwargs` - this already works for `BaseStringMessagePromptTemplate` and `BaseMessage` but this wasn't yet wired up to `BasePromptTemplate`
I added this field and also allowed `ChatPromptTemplate.from_messages()` (derived from `BasePromptTemplate`) to pass on `**kwargs` so that you could use it in that way, also consistent with other classmethods in that file.
Tagging @hwchase17 and @eyurtsev for template/prompt stuff
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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PR_kwDOIPDwls5Yad4o
| 9,554 |
Reminder to not report security issues as "bug" type issues.
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[] | null | 1 | 2023-08-21T17:53:36 | 2023-08-21T19:48:34 | 2023-08-21T19:48:33 |
COLLABORATOR
| null |
Updated the issue template that pops up when users open a new issue.
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I_kwDOIPDwls5u2zuM
| 9,553 |
using PromptTemplate with initialze_agent
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[] | null | 8 | 2023-08-21T17:47:57 | 2023-11-27T16:06:21 | 2023-11-27T16:06:20 |
NONE
| null |
### System Info
I am trying to use a template to pre customize the AI, but my Code is not working!
Any idea why it's not working?
This is the basic part of my code:
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
template = """You are a customer service representative working for Amazon. You are having conversations with customers.
When asked about your profession, you should respond that you are a customer service representative for Amazon.
{memory}
#Human: {human_input}
#Chatbot:"""
prompt = PromptTemplate(input_variables=["memory", "human_input"],template=template)
memory = ConversationBufferMemory(memory_key="memory",prompt=prompt, return_messages=True)
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613",verbose=True)
def get_agent(self):
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
}
agent = initialize_agent(
tools=self.tools,
llm=llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
agent_kwargs=agent_kwargs,
memory=memory,
prompt=prompt,
)
return agent
### Expected behavior
When I ask the AI what's her prefession is, she is telling
"I am an AI assistant designed to provide helpful information and assist with various tasks" istead of
"you are a customer service representative for Amazon"
|
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| 1,859,856,978 |
PR_kwDOIPDwls5Yaaq3
| 9,552 |
Require manually triggering release workflows.
|
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[] | null | 1 | 2023-08-21T17:45:38 | 2023-08-21T17:54:45 | 2023-08-21T17:54:44 |
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| 1,859,842,636 |
PR_kwDOIPDwls5YaXdW
| 9,551 |
Add `SECURITY.md` file to the repo.
|
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[] | null | 1 | 2023-08-21T17:38:06 | 2023-08-21T17:40:00 | 2023-08-21T17:39:59 |
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| 1,859,842,021 |
I_kwDOIPDwls5u2u_l
| 9,550 |
llama 2 through HF further integration with langchain on chat model
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[] | null | 2 | 2023-08-21T17:37:45 | 2023-11-27T16:06:26 | 2023-11-27T16:06:25 |
NONE
| null |
### Feature request
appreciate your efforts on show how integrate Hugginface models with langchain: https://python.langchain.com/docs/integrations/llms/huggingface_pipelines
but this is just a shallow port from huggingface to langchain not yet a full integration on the [Chat model](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/chat_models) class that require further inherence and special case development.
Can you guys spare some efforts to integrate the self-hosted version of llama 2 into the chat model?
### Motivation
How self-hosted llama 2 to fully integrate with langchain for avoid privacy and control the whole llm app development cycles.
### Your contribution
I'm willing to help to submit PR with proper guidance.
|
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PR_kwDOIPDwls5YaP9z
| 9,549 |
bump 270
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PR_kwDOIPDwls5YZ_wz
| 9,548 |
Add function_call to FakeListChatModel
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[] | null | 3 | 2023-08-21T16:29:26 | 2023-11-10T22:04:33 | 2023-11-10T22:04:33 |
NONE
| null |
This PR allows users to simulate function calls using the `FakeListChatModel` by passing in dictionary values as the responses.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
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2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5YZ9kT
| 9,547 |
docs: `MLflow API` and examples
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[] | null | 3 | 2023-08-21T16:22:49 | 2023-09-04T17:08:34 | 2023-09-04T03:52:21 |
COLLABORATOR
| null |
Added docs and links to the API and examples provided by MLflow itself
|
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| 1,859,715,484 |
I_kwDOIPDwls5u2QGc
| 9,546 |
Being able to retry a chain until the output is valid
|
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[] | null | 5 | 2023-08-21T16:17:22 | 2023-11-22T15:17:59 | null |
NONE
| null |
### Feature request
The RetryOutputParser class has several drawbacks:
- it must point to a chain different from the one that calls it
- it does not make it possible to specify a number of retries
### Motivation
Following a request, ChatGPT sometimes answers with an invalid format and the same request must be resent to get the expected output.
It would be useful to have a new parameter added to a LLMChain (endowed with an output parser) to be able to retry the chain till the output is validated by the parser (i.e. it does not trigger any exception), with the possibility to specify a maximum of retries. It could take this form (just a proposal):
```
chain = LLMChain(llm=..., prompt=..., output_parser=..., retry=RetryChain(max_tries=10))
```
### Your contribution
Well, I don't think this feature would be complex to develop for the developers that already know the code. Let me know if this request makes sense.
|
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I_kwDOIPDwls5u2Imb
| 9,545 |
Azure cognitive search retriever: ValidationError: 1 validation error for Document page_content
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[] | null | 2 | 2023-08-21T15:58:11 | 2023-08-21T19:33:47 | 2023-08-21T19:32:44 |
NONE
| null |
### Issue you'd like to raise.
I am using the Cognitive search retriever according to the documentation, however I run into an error, regarding the type of return value.
**Reproduction**:
import os
from langchain.retrievers import AzureCognitiveSearchRetriever
os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"] = ""
os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"] = ""
os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"] = ""
retriever = AzureCognitiveSearchRetriever(content_key="text", top_k=5)
retriever.get_relevant_documents(query="What is langchain?")
**Expected behaviour**: return relevant documents/snippets
**Actual behaviour**:
ValidationError: 1 validation error for Document
page_content
str type expected (type=type_error.str)
It seems like the langchain retriever expects to receive somewhere a str, where azure cognitive search returns something else. Can anyone help/explain? The error message does not tell what type it receives and where the string should be, it would like to receive
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5YZz-G
| 9,544 |
Release on push to `master` instead of on closed PRs targeting it.
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[] | null | 1 | 2023-08-21T15:55:48 | 2023-08-21T16:57:41 | 2023-08-21T16:57:40 |
COLLABORATOR
| null |
This is safer than the prior approach, since it's safe by default: the release workflows never get triggered for non-merged PRs, so there's no possibility of a buggy conditional accidentally letting a workflow proceed when it shouldn't have.
The only loss is that publishing no longer requires a `release` label on the merged PR that bumps the version. We can add a separate CI step that enforces that part as a condition for merging into `master`, if desirable.
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| 1,859,637,622 |
PR_kwDOIPDwls5YZqiX
| 9,543 |
Fix conditional that erroneously always runs.
|
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[] | null | 1 | 2023-08-21T15:30:09 | 2023-08-21T17:24:34 | 2023-08-21T17:24:33 |
COLLABORATOR
| null |
The input it means to test for is `"libs/langchain"` and not `"langchain"`.
|
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| 1,859,608,504 |
PR_kwDOIPDwls5YZkBW
| 9,542 |
Nc/small fixes 21aug
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[] | null | 1 | 2023-08-21T15:15:56 | 2023-08-21T17:01:21 | 2023-08-21T17:01:20 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,859,531,876 |
I_kwDOIPDwls5u1jRk
| 9,540 |
Impossible to enrich BaseOutputParser with a custom object member
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[] | null | 3 | 2023-08-21T14:43:51 | 2023-11-20T18:19:46 | null |
NONE
| null |
### System Info
Langchain 0.0.268
Python 3.9
Windows 10
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.schema import BaseOutputParser
class CustomType:
pass
class CustomParser(BaseOutputParser):
model_config = ConfigDict(arbitrary_types_allowed=True)
c : CustomType
```
### Expected behavior
This triggers a RuntimeError since no validator could be found for CustomType object. It prevents the user from passing custom types to the constructor of a langchain's output parser.
Though, this problem has been corrected in the latest versions of pydantic (thanks to the flag arbitrary_types_allowed). After investigating the source code, one of the mother classes of BaseOutputParser is Serializable and the latter derives from... pydantic.v1.BaseModel instead of pydantic.BaseModel. Why is that? This seems to be the source of the problem.
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https://api.github.com/repos/langchain-ai/langchain/issues/9539
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| 1,859,508,257 |
PR_kwDOIPDwls5YZNrI
| 9,539 |
Add exclude to GenericLoader.from_file_system
|
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| false | null |
[] | null | 1 | 2023-08-21T14:31:00 | 2023-08-23T23:09:11 | 2023-08-23T23:09:10 |
COLLABORATOR
| null |
- Exclude in Generic from_filesystem
- Update generic.py
- x
|
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| 1,859,484,256 |
I_kwDOIPDwls5u1Xpg
| 9,538 |
Issue: I am using GPT-4 with SQLDatabaseChain it shows ConnectionAbortedError: [WinError 10053] An established connection was aborted by the software in your host machine error
|
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| false | null |
[] | null | 2 | 2023-08-21T14:18:28 | 2023-11-27T16:06:31 | 2023-11-27T16:06:31 |
NONE
| null |
### Issue you'd like to raise.
I am working with SQLDatabaseChain in Django framework everytime I execute the code it shows below error.
File "C:\Users\ehsuser\AppData\Local\Programs\Python\Python310\lib\socket.py", line 705, in readinto
return self._sock.recv_into(b)
ConnectionAbortedError: [WinError 10053] An established connection was aborted by the software in your host machine
Below is my code
user='root'
database= 'real_estate_chatbot_new'
password=''
host= 'localhost'
port= '3306'
table_name1='app_projects'
table_name2='app_projectimages'
db_uri=f"mysql+pymysql://{user}:{password}@{host}:{port}/{database}"
#input_db = SQLDatabase.from_uri('sqlite:///ashridhar.db')
input_db = SQLDatabase.from_uri(db_uri,include_tables=[table_name1,table_name2])
db_chain = SQLDatabaseChain.from_llm(llm, input_db, verbose=True)
prompt = """
- You're a real estate chatbot for Buy home that is going to answer to a potential lead so keep your messages dynamic and enthusiastic making the interactions lively and enjoyable!.
- Answer the question from database only if you don't find answer from database return a friendly message.
- don't use word like 'database' in answer.
- If it is not relevant to the database return connvencing and gratitude message.
- If it is realted to price return it in words suppose the price is 7000000 then return 70 lakhs."""
tools = [
Tool(
name="Real_Estate_Chatbot",
func=db_chain.run,
#description="Answer the question from database only if you don't find answer from database return a friendly message., You're a real estate sales agent that is going to answer to a potential lead so keep your messages dynamic and enthusiastic making the interactions lively and enjoyable!.",
description=prompt
),
]
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
}
memory = ConversationBufferWindowMemory(memory_key="memory",k=4,return_messages=True)
agent = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
agent_kwargs=agent_kwargs,
memory=memory,
)
response = agent.run(message)
return response
### Suggestion:
_No response_
|
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| 1,859,472,931 |
I_kwDOIPDwls5u1U4j
| 9,537 |
Plain models are not calling callbacks when cached
|
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open
| false | null |
[] | null | 7 | 2023-08-21T14:12:46 | 2024-01-02T09:20:43 | null |
CONTRIBUTOR
| null |
### System Info
```
pip freeze | grep langchain
langchain==0.0.268
langchainplus-sdk==0.0.17
```
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```py
from typing import Any, Dict, List
import langchain
from langchain.cache import SQLiteCache
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatVertexAI
from langchain.llms import VertexAI
from langchain.schema import HumanMessage
langchain.llm_cache = SQLiteCache(database_path="langchain-llm-cache.db")
class CallbackHandler(BaseCallbackHandler):
run_inline = True
def __init__(self, logger=None):
self.messages = []
def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], run_id, parent_run_id, **kwargs: Any) -> Any:
self.messages.append(run_id)
callback = CallbackHandler()
bison_text_llm = VertexAI(model_name="text-bison@001", temperature=0.0, max_output_tokens=500, callbacks=[callback])
bison_chat_llm = ChatVertexAI(model_name="chat-bison@001", temperature=0.0, max_output_tokens=500, callbacks=[callback])
bison_chat_llm([HumanMessage(content="Hello, how are you?")])
assert len(callback.messages) == 1
bison_chat_llm([HumanMessage(content="Hello, how are you?")])
assert len(callback.messages) == 2
bison_text_llm("Hello, how are you?")
assert len(callback.messages) == 3
bison_text_llm("Hello, how are you?")
assert len(callback.messages) == 4
```
### Expected behavior
It's unclear to me whether callbacks should be called when call is cached, but we can see that chat and plain text models implement different behaviour.
Ideally, callbacks would be called, with a flag saying that call is cached.
|
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| 1,859,467,730 |
PR_kwDOIPDwls5YZE25
| 9,536 |
feat(streaming_aiter.py): add support for multiple LLM runs in a AsyncIteratorCallbackHandler
|
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[] | null | 3 | 2023-08-21T14:10:28 | 2023-10-18T00:06:50 | 2023-09-04T03:58:55 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
- Description: When I want to integrate LangChain into my Poe-API Project, which aims to provide multiple OpenAI functions for users, I write code based on https://github.com/langchain-ai/langchain-template-poe-fastapi/blob/main/langchain_template_poe_fastapi/handler.py
```py
class EchoBot(PoeBot):
async def get_response(self, query):
callback_handler = AsyncIteratorCallbackHandler()
mode = ChatOpenAI(
temperature=0,
model="gpt-3.5-turbo",
streaming=True,
callbacks=[callback_handler],
)
memory = convert_poe_messages(query.query)
agent = initialize_agent(
tools,
llm=mode,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
agent_kwargs={
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
},
memory=memory,
)
logging.info(query.query)
last_message = query.query[-1].content
run = asyncio.create_task(agent.arun(last_message))
# Yield the tokens as they come in
async for token in callback_handler.aiter():
yield self.text_event(token)
# Await the chain run
await run
```
However, I have noticed that it is not functioning properly. Example of each run process: `__init__ -> aiter -> on_llm_start -> on_llm_end -> on_llm_start ->on_llm_end`, `aiter` will end at the first `on_llm_end`.
The purpose of this PR is to ensure that `aiter` does not terminate at the first occurrence of `on_llm_end` when `LLMResult` has no any output message.
- Issue: https://github.com/langchain-ai/langchain/issues/9374
- Dependencies: None
|
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| 1,859,456,565 |
I_kwDOIPDwls5u1Q41
| 9,535 |
Regression in structured_chat agent's Output parser
|
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[] | null | 2 | 2023-08-21T14:05:33 | 2023-11-27T16:06:36 | 2023-11-27T16:06:35 |
NONE
| null |
### System Info
This regression affects Langchain >=0.0.262. This regression was introduced with #8965.
If an agent's output to input to a tool (e.g. to generate an `AgentAction`) contains either backticks (such as to represent a code block with ```), or embedded JSON (such as a structured JSON string in the `action_input` key), then the output parsing will fail.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behavior:
1. Generate an agent that outputs a markdown formatted code block with a language.
To input to this tool, an `AgentAction` step will be made, that might look like:
```
```json
{
"action": "Code generator tool",
"action_input": "Generate code to optimize ```python\nprint("hello world")```
}
```
(Can't show closing backticks above due to formatting issues, but assume there's closing backticks in the above code block)
2. An error will occur, as a result of being unable to parse the actions.
Using
`pattern = re.compile(r"```(?:json)?\n(.*)```", re.DOTALL)` works slightly better for both embedded JSON and backticks, but will result in unexpected behavior if there are multiple actions in a response
### Expected behavior
Should be able to parse a response with backticks or JSON inside the `action_input` key
|
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| 1,859,412,229 |
PR_kwDOIPDwls5YY4X2
| 9,534 |
Add CrateDB prompt
|
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closed
| false | null |
[] | null | 2 | 2023-08-21T13:46:45 | 2023-08-22T13:42:57 | 2023-08-22T13:42:57 |
CONTRIBUTOR
| null |
Adds a prompt template for the CrateDB SQL dialect.
|
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| 1,859,248,153 |
I_kwDOIPDwls5u0eAZ
| 9,532 |
SelfQueryRetriever returns duplicate document data using with chromaDB
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[] | null | 5 | 2023-08-21T12:15:55 | 2023-11-28T16:08:10 | 2023-11-28T16:08:09 |
NONE
| null |
### System Info
Langchain version = 0.0.268
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have list of objects in roadmap.json and I tried to achieve the what was given in selfquery retriver documentation
```
with open('roadmap.json') as json_file:
allRoadmap = json.load(json_file)
docs = []
for roadmap in allRoadmap:
print(roadmap["name"])
for section in roadmap["sections"]:
single_doc = Document(
page_content=f"This section is related to {roadmap['name']}",
metadata={"roadmapName": roadmap["name"], "sectionTopic": section["name"]}
)
docs.append(single_doc)
print(section["name"])
print("\n")
vectorstore = Chroma.from_documents(docs, embeddings)
print(vectorstore)
for doc in docs:
print(doc)
```
This above code outputs
```
page_content='This section is related to HTML for beginners' metadata={'roadmapName': 'HTML for beginners', 'sectionTopic': 'HTML Basics'}
page_content='This section is related to HTML for beginners' metadata={'roadmapName': 'HTML for beginners', 'sectionTopic': 'HTML Tags and Elements'}
page_content='This section is related to HTML for beginners' metadata={'roadmapName': 'HTML for beginners', 'sectionTopic': 'Intermediate Concepts'}
page_content='This section is related to HTML for beginners' metadata={'roadmapName': 'HTML for beginners', 'sectionTopic': 'Advanced Concepts'}
page_content='This section is related to How Search Engine Works' metadata={'roadmapName': 'How Search Engine Works', 'sectionTopic': 'Internet'}
page_content='This section is related to How Search Engine Works' metadata={'roadmapName': 'How Search Engine Works', 'sectionTopic': 'Search Engine'}
page_content='This section is related to Github Roadmap' metadata={'roadmapName': 'Github Roadmap', 'sectionTopic': 'Section 1'}
page_content='This section is related to Bootstrap' metadata={'roadmapName': 'Bootstrap', 'sectionTopic': 'Introduction'}
page_content='This section is related to Bootstrap' metadata={'roadmapName': 'Bootstrap', 'sectionTopic': 'Concepts'}
page_content='This section is related to Bootstrap' metadata={'roadmapName': 'Bootstrap', 'sectionTopic': 'Hands On'}
page_content='This section is related to MongoDB' metadata={'roadmapName': 'MongoDB', 'sectionTopic': 'Introduction'}
page_content='This section is related to MongoDB' metadata={'roadmapName': 'MongoDB', 'sectionTopic': 'Queries'}
page_content='This section is related to MongoDB' metadata={'roadmapName': 'MongoDB', 'sectionTopic': 'Data Modeling'}
page_content='This section is related to MongoDB' metadata={'roadmapName': 'MongoDB', 'sectionTopic': 'Aggregation'}
page_content='This section is related to Python for Beginers' metadata={'roadmapName': 'Python for Beginers', 'sectionTopic': 'Getting Started'}
page_content='This section is related to Python for Beginers' metadata={'roadmapName': 'Python for Beginers', 'sectionTopic': 'Basics'}
page_content='This section is related to Python for Beginers' metadata={'roadmapName': 'Python for Beginers', 'sectionTopic': 'Conditions and Loops'}
page_content='This section is related to Python for Beginers' metadata={'roadmapName': 'Python for Beginers', 'sectionTopic': 'Arrays'}
page_content='This section is related to Python for Beginers' metadata={'roadmapName': 'Python for Beginers', 'sectionTopic': 'Functions'}
page_content='This section is related to Python for Beginers' metadata={'roadmapName': 'Python for Beginers', 'sectionTopic': 'Solving Problems'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Basics'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Color and Background'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Typography and Fonts'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Spacing in CSS'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Basic Styling'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Positioning Techniques'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Advanced Styling'}
page_content='This section is related to Google Colab' metadata={'roadmapName': 'Google Colab', 'sectionTopic': 'Section 1'}
page_content='This section is related to MySQL' metadata={'roadmapName': 'MySQL', 'sectionTopic': 'Section 1'}
page_content='This section is related to Docker' metadata={'roadmapName': 'Docker', 'sectionTopic': 'Section 1'}
page_content='This section is related to AWS Lambda' metadata={'roadmapName': 'AWS Lambda', 'sectionTopic': 'Section 1'}
page_content='This section is related to Java' metadata={'roadmapName': 'Java', 'sectionTopic': 'Section 1'}
```
I tried to retrieve data from using selfQuery
```
from langchain.llms import OpenAI
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
metadata_field_info = [
AttributeInfo(
name="sectionTopic",
description="The section topic of the roadmap",
type="string",
),
AttributeInfo(
name="roadmapName",
description="Name of the roadmap",
type="string",
),
]
document_content_description = "Roadmap section topics"
llm = OpenAI(temperature=0.1)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True,
search_kwargs={"k": 7}
)
retriever.get_relevant_documents("What are unique section topics that are related to css")
```
For the above code it gave repeated document.
```
query='css' filter=None limit=None
[Document(page_content='This section is related to CSS for beginners', metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Advanced Styling'}),
Document(page_content='This section is related to CSS for beginners', metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Typography and Fonts'}),
Document(page_content='This section is related to CSS for beginners', metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Basic Styling'}),
Document(page_content='This section is related to CSS for beginners', metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Color and Background'}),
Document(page_content='This section is related to CSS for beginners', metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Spacing in CSS'}),
Document(page_content='This section is related to CSS for beginners', metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Basic Styling'}),
Document(page_content='This section is related to CSS for beginners', metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Advanced Styling'})]
```
### Expected behavior
It should return 7 unique documents
```
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Basics'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Color and Background'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Typography and Fonts'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Spacing in CSS'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Basic Styling'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Positioning Techniques'}
page_content='This section is related to CSS for beginners' metadata={'roadmapName': 'CSS for beginners', 'sectionTopic': 'Advanced Styling'}
```
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| 1,859,210,421 |
PR_kwDOIPDwls5YYLuy
| 9,531 |
tair fix distance_type error, and add hybrid search
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| false | null |
[] | null | 2 | 2023-08-21T11:52:30 | 2023-08-23T23:38:32 | 2023-08-23T23:38:32 |
CONTRIBUTOR
| null |
- fix: distance_type error,
- feature: Tair add hybrid search
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PR_kwDOIPDwls5YX21_
| 9,530 |
All prompts translated to russian
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closed
| false | null |
[] | null | 1 | 2023-08-21T10:53:36 | 2023-08-22T08:10:44 | 2023-08-21T11:04:09 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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[] | null | 1 | 2023-08-21T10:49:53 | 2023-08-21T10:59:57 | 2023-08-21T10:51:22 |
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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I_kwDOIPDwls5uzNAO
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AttributeError: 'RecursiveCharacterTextSplitter' object has no attribute 'split_documents'
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[] | null | 2 | 2023-08-21T08:54:33 | 2023-11-27T16:06:46 | 2023-11-27T16:06:45 |
NONE
| null |
### System Info
LangChain 0.0.74
Python 3.10
Windows 10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders import UnstructuredExcelLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
loader = UnstructuredExcelLoader("N:\Python\Data.xlsx", mode="elements")
index = VectorstoreIndexCreator().from_loaders([loader])
Gives the following:
Traceback (most recent call last):
Cell In[33], line 1
index = VectorstoreIndexCreator().from_loaders([loader])
File C:\Program Files\Anaconda3\lib\site-packages\langchain\indexes\vectorstore.py:73 in from_loaders
File C:\Program Files\Anaconda3\lib\site-packages\langchain\indexes\vectorstore.py:77 in from_documents
AttributeError: 'RecursiveCharacterTextSplitter' object has no attribute 'split_documents'
### Expected behavior
Hi,
When using the VectorstoreIndexCreator, I get an error:
AttributeError: 'RecursiveCharacterTextSplitter' object has no attribute 'split_documents'
In this case, it is when I want to upload an excel file, but I get the same when trying to upload a .txt get the same error when I try to upload .txt files (with TextLoader).
Many thanks for your help!
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PR_kwDOIPDwls5YXCUU
| 9,527 |
Add AINetwork blockchain toolkit integration
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[] | null | 3 | 2023-08-21T08:31:14 | 2023-08-22T15:08:38 | 2023-08-22T15:03:34 |
CONTRIBUTOR
| null |
# Description
This PR introduces a new toolkit for interacting with the AINetwork blockchain. The toolkit provides a set of tools for performing various operations on the AINetwork blockchain, such as transferring AIN, reading and writing values to the blockchain database, managing apps, setting rules and owners.
# Dependencies
[ain-py](https://github.com/ainblockchain/ain-py) >= 1.0.2
# Misc
The example notebook (langchain/docs/extras/integrations/toolkits/ainetwork.ipynb) is in the PR
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I_kwDOIPDwls5uy2a0
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Chatbot responses are deterorating on using langchain compared to open AI completion
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[] | null | 2 | 2023-08-21T08:00:24 | 2023-11-30T16:07:01 | 2023-11-30T16:07:00 |
NONE
| null |
### Issue you'd like to raise.
Hi,
Iam using langchain version 0.0.187.
Here are the steps that I have followed to get chat response uisng langchain
```
def initlize_llm_chain_prompt(self):
print("initilizing llm chain prompt")
llm_model=AzureOpenAI(deployment_name=self.AZURE_OPENAI_CHATGPT_DEPLOYMENT,model_name=os.environ.get("AZURE_OPENAI_CHATGPT_MODEL"),
temperature=0,
max_tokens=1000)
llm_chat_prompt = PromptTemplate(input_variables=["question"], template=self.prompt_template)
question_generator = LLMChain(llm=llm_model, prompt=llm_chat_prompt)
doc_chain=load_qa_chain(llm_model, chain_type="stuff")
memory = ConversationBufferWindowMemory(memory_key="chat_history",return_messages=True,k=4) # remember last 4 conversations
self.llm_chain_prompt=ConversationalRetrievalChain(retriever=self.elastic_vector_search.as_retriever(search_kwargs={"k": 5}),
memory=memory,question_generator=question_generator,combine_docs_chain=doc_chain,verbose=True,get_chat_history=self.get_chat_history_custom)
```
**_Here is how Iam calling chat_**
`result=self.llm_chain({"question": user_query}, )`
Here is the Prompt template format
```
prompt_template = """<|im_start|>System
Answer ONLY with the facts listed in the Referenced documents.
If there isn't enough information in the Sources , say you don't know.
Do not strictly generate answers if its not available in the Source and even though if you know the answer for it.
<|im_end|>
<|im_start|>
UserQuery:
{question}
<|im_end|>
<|im_start|>Chatbot:
"""
```
In get_chat_history_custom() Iam just appending the conversation history data
```
def get_chat_history_custom(self,inputs):
res = []
if(len(inputs)>0):
inp_len=len(inputs)
for i in range(0,inp_len,2):
human_content = inputs[i].content
ai_content=inputs[i+1].content.split('Question')[0]
res.append(f"Human:{human_content}\AIMessage:{ai_content}")
buf="\n".join(res)
return buf
else:
return ""
```
What I have observed is Iam not getting good responses even though Iam providing the relavent data as the source to the chat. Iam getiing good and expected answers if I use open AI completion call. Can I please knoww, If any of the above steps is wrong? and Do you recommond any other version langchain?
### Suggestion:
Chatbot responses are not getting as expected in langchain on compared to the responses of open AI completion
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[] | null | 1 | 2023-08-21T07:20:34 | 2023-08-21T19:40:45 | 2023-08-21T19:40:44 |
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I_kwDOIPDwls5uyNGd
| 9,523 |
Request for using custom fine tuned models
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[] | null | 2 | 2023-08-21T05:59:17 | 2023-11-27T16:06:56 | 2023-11-27T16:06:56 |
NONE
| null |
### Feature request
Issue you'd like to raise.
langchain==0.0.162
Hi, I am using a fine tuned model that generates user prompts to SQL queries, instead of the default model provided by langchain. The reason for doing this is because langchain does not know about all the data from the database unless you provide context to it but there is a lot of data hence why it can create incorrect SQL queries also it is unable to form complex queries for similar reasons even after you give it context.
So my question is based upon the output I am getting below. Is there a way to keep the initial question I asked the same throughout like in the action input, instead of "SOME_BRANCH_NAME" I want the entire sentence to go through to the SQLDatabaseChain like the user initially asked which is "what is the summary of last 3 issues reported by SOME_BRANCH_NAME". Basically since the Action Input is different from what the user asked, it is generating the wrong SQL query since what it should be doing is this, "SELECT summary FROM sla_tat_summary WHERE organization like '%SOME_BRANCH_NAME%' ORDER BY ReportedDate DESC LIMIT 3;" instead of what is shown below. I could just use the SQLDatabaseChain on its own which does get the exact query I want since I was able to make sure only the prompt the user gave went through, but the agent is needed since I am using it for things other than SQL generation.
user prompt: what is the summary of last 3 issues reported by SOME_BRANCH_NAME
Entering new AgentExecutor chain...
I need to find out what the last 3 issues reported by SOME_BRANCH_NAME were.
Action: TPS Issue Tracker Database
Action Input: SOME_BRANCH_NAME
Entering new SQLDatabaseChain chain...
SOME_BRANCH_NAME:
SELECT organization, COUNT() FROM sla_tat_summary WHERE severity = 'Level 2 - Critical' GROUP BY organization ORDER BY COUNT() DESC LIMIT 1
In summary I want to have an option to keep my user prompt unchanged throughout the flow from the agent to the to SQLdatabasechain
### Motivation
I need a way for langchain to be able to use fine-tuned models for multi-class classification and have a way to not use hard-coded stopping points rather have it as a parameter for a developer to select.
### Your contribution
my custom langchain package would only integrate well with my own use case hence why I am not submitting a PR. Also even though I am using langchain==0.0.162. But the issue would be similar in the latest langchain version
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I_kwDOIPDwls5uxudq
| 9,522 |
Issue: Proxy not successfully used
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[] | null | 6 | 2023-08-21T03:49:38 | 2023-11-28T16:08:20 | 2023-11-28T16:08:19 |
NONE
| null |
### Issue you'd like to raise.
This is my code, I'm not sure if it's correct:
`from langchain.llms import ChatGLM
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType, tool
from datetime import date
@tool
def time(text: str) -> str:
"""返回今天的日期，用于回答用户关于今天日期的问题。
输入应该始终为空字符串，此函数将始终返回今天的日期。
任何日期计算都应在此函数外部进行。"""
return str(date.today())
endpoint_url = "http://0.0.0.0:8000"
llm = ChatGLM(
endpoint_url=endpoint_url,
max_token=80000,
# history=[["我有一些问题！","我可以回答你的任何问题，请向我提问！"]],
top_p=0.9,
model_kwargs={"sample_model_args": False},
)
tools = load_tools([], llm=llm)
agent = initialize_agent(
tools + [time],
llm,
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True,
verbose=True
)
try:
result = agent.run("今天是几号？")
print(result)
except Exception as e:
print("外部访问异常：", e)`
However, the answer after running is not accurate：
`(chatglm_env) root@autodl-container-b25f1193e8-0ae83321:~/autodl-tmp# python t_n.py
> Entering new AgentExecutor chain...
Could not parse LLM output: 今天是2023年2月18日。
Observation: Invalid or incomplete response
Thought:Could not parse LLM output: Today is February 18th, 2023.
Observation: Invalid or incomplete response
Thought:Final Answer: 今天是2023年2月18日。
> Finished chain.
今天是2023年2月18日。`
Please help me
### Suggestion:
_No response_
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PR_kwDOIPDwls5YVmLH
| 9,521 |
fix: Updated marqo integration for marqo version 1.0.0+
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[] | null | 2 | 2023-08-21T02:34:46 | 2023-08-21T17:43:19 | 2023-08-21T17:43:15 |
CONTRIBUTOR
| null |
- Description: Updated marqo integration to use tensor_fields instead of non_tensor_fields. Upgraded marqo version to 1.2.4
- Dependencies: marqo 1.2.4
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AttributeError: 'super' object has no attribute 'parse_obj' when using OpenAPISpec.from_file
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[] | null | 11 | 2023-08-21T02:28:34 | 2024-01-14T18:32:49 | null |
NONE
| null |
### System Info
Langchain version 0.0.268
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Load in .yaml file containing the OpenAPI specification.
2. Call the OpenAPISpec with ` open_api_spec = OpenAPISpec.from_file(file_path)`
Error shows that in _openapi.py_ line 202 ,`return super().parse_obj(obj)` no longer inherits the super method from Pydantic's BaseModel parse_obj.
### Expected behavior
The OpenAPISpec should be delivered so that it can be passed on to `NLAToolkit.from_llm_and_spec`.
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I_kwDOIPDwls5uxWwd
| 9,519 |
FAISS vectorstore `similarity_search_with_relevance_scores` returns strange/false result with `MAX_INNER_PRODUCT`
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[] | null | 3 | 2023-08-21T02:21:28 | 2023-12-25T16:09:00 | 2023-12-25T16:08:59 |
CONTRIBUTOR
| null |
### System Info
langchain==0.0.268
python=3.9
mac
### Who can help?
I used the FAISS as the vector store. it seems that the `similarity_search_with_score` (supposedly ranked by distance: low to high) and `similarity_search_with_relevance_scores`((supposedly ranked by relevance: high to low) produce conflicting results when specifying `MAX_INNER_PRODUCT` as the distance strategy. Please see the screenshot below:
<img width="675" alt="Screenshot 2023-08-20 at 6 54 15 PM" src="https://github.com/langchain-ai/langchain/assets/7220686/88981277-a0b4-462b-929c-63bd19d4faff">
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.vectorstores.faiss import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
embedding_engine = HuggingFaceEmbeddings(
model_name= "BAAI/bge-base-en", #"sentence-transformers/all-mpnet-base-v2",
model_kwargs={"device": "cpu"},
encode_kwargs={'normalize_embeddings': True} # set True to compute cosine similarity
)
texts = ["I like apples", "I hate apples", "I like oranges"]
simple_vecdb = FAISS.from_texts(texts,
embedding_engine,
distance_strategy="MAX_INNER_PRODUCT")
# test 1
simple_vecdb.similarity_search_with_score("I like apples")
# test 2
simple_vecdb.similarity_search_with_relevance_scores("I like apples")
### Expected behavior
For similarity_search_with_score, if the `similarity_search_with_score` documentation is correct saying that "List of documents most similar to the query text with L2 distance in float. Lower score represents more similarity.", then the result from `similarity_search_with_score` should have matched the identical text `I like apple` with `(distance)score=0` and the `similarity_search_with_relevance_scores` with `(relevance)score=1`, no?
if the FAISS return the cosine similarity score already during the `similarity_search_with_score`call, should the `_max_inner_product_relevance_score_fn` for the FAISS just return the identical score instead of `1-score` when calculating the relevance score?
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PR_kwDOIPDwls5YVNdZ
| 9,518 |
beef up retrieval docs
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[] | null | 1 | 2023-08-21T00:21:19 | 2023-08-21T14:22:23 | 2023-08-21T14:22:23 |
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PR_kwDOIPDwls5YVJxU
| 9,516 |
`Qwen` model example
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[] | null | 1 | 2023-08-20T23:43:54 | 2023-08-21T01:56:16 | 2023-08-21T00:21:45 |
COLLABORATOR
| null |
added an example for `Qwen-7B` model on `HugginfFaceHub` :hugs:
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PR_kwDOIPDwls5YU7te
| 9,515 |
Anthropic: Allow the use of kwargs consistent with ChatOpenAI.
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[] | null | 2 | 2023-08-20T19:21:54 | 2023-08-24T01:23:22 | 2023-08-24T01:23:21 |
CONTRIBUTOR
| null |
- Description: ~~Creates a new root_validator in `_AnthropicCommon` that allows the use of `model_name` and `max_tokens` keyword arguments.~~ Adds pydantic field aliases to support `model_name` and `max_tokens` as keyword arguments. Ultimately, this makes `ChatAnthropic` more consistent with `ChatOpenAI`, making the two classes more interchangeable for the developer.
- Issue: https://github.com/langchain-ai/langchain/issues/9510
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PR_kwDOIPDwls5YUq19
| 9,513 |
Document loaders confluence keep newlines
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[] | null | 2 | 2023-08-20T14:57:45 | 2023-08-21T19:41:11 | 2023-08-21T19:41:10 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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If you're adding a new integration, please include:
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PR_kwDOIPDwls5YUlg_
| 9,512 |
Fix timeout by retrying policy
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[] | null | 2 | 2023-08-20T13:29:14 | 2023-08-20T23:22:10 | 2023-08-20T22:57:21 |
NONE
| null |
- Description: Fix timeout by retrying policy
- Issue: the issue #9509
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I_kwDOIPDwls5uwJSi
| 9,511 |
pgvector extension is not installed
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[] | null | 2 | 2023-08-20T12:52:42 | 2023-12-06T17:44:06 | 2023-12-06T17:44:05 |
NONE
| null |
https://github.com/langchain-ai/langchain/blame/e51bccdb2890fa193ce7eb5bf7e13c28afef4dc4/libs/langchain/langchain/vectorstores/pgvector.py#L117
@hwchase17
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| 1,858,110,406 |
I_kwDOIPDwls5uwIPG
| 9,510 |
missing model_name param in ChatAnthropic
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[] | null | 4 | 2023-08-20T12:37:45 | 2023-12-07T16:06:40 | 2023-12-07T16:06:39 |
NONE
| null |
### System Info
Python 3.8.16
langchain==0.0.268
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`ChatAnthropic` doesn't have `model_name` attribute. instead, it has a `model` attribute that specifies the name. Other chat models such as `ChatOpenAI`, `ChatVertexAI` has `model_name` attribute, this breaks the interface while integrating with multiple LLMs.
### Expected behavior
`llm = ChatAnthropic(model_name='claude-2')`
`print(llm.model_name)`
|
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I_kwDOIPDwls5uwEx5
| 9,509 |
TimeOutError unhandled
|
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[] | null | 2 | 2023-08-20T11:50:21 | 2023-11-26T16:06:29 | 2023-11-26T16:06:28 |
NONE
| null |
### System Info
`ValueError Traceback (most recent call last)
Cell In[35], line 3
1 query = "What did the president say about the Supreme Court"
2 docs = db.similarity_search(query)
----> 3 chain.run(input_documents=docs, question=query)
File [~/.local/lib/python3.10/site-packages/langchain/chains/base.py:480](https://file+.vscode-resource.vscode-cdn.net/home/moghadas/toy_projects/~/.local/lib/python3.10/site-packages/langchain/chains/base.py:480), in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
475 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
476 _output_key
477 ]
479 if kwargs and not args:
--> 480 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
481 _output_key
482 ]
484 if not kwargs and not args:
485 raise ValueError(
486 "`run` supported with either positional arguments or keyword arguments,"
487 " but none were provided."
488 )
File [~/.local/lib/python3.10/site-packages/langchain/chains/base.py:282](https://file+.vscode-resource.vscode-cdn.net/home/moghadas/toy_projects/~/.local/lib/python3.10/site-packages/langchain/chains/base.py:282), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
280 except (KeyboardInterrupt, Exception) as e:
281 run_manager.on_chain_error(e)
--> 282 raise e
...
113 if self.client.task == "text-generation":
114 # Text generation return includes the starter text.
115 text = response[0]["generated_text"][len(prompt) :]
ValueError: Error raised by inference API: Model google/flan-t5-xl time out`
This exception should be handled
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`query = "What did the president say about the Supreme Court"
docs = db.similarity_search(query)
chain.run(input_documents=docs, question=query)`
### Expected behavior
Retry mechanism, or caching
|
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PR_kwDOIPDwls5YUVp3
| 9,508 |
Fixing class calling error in the documentation of connecting_to_a_feature_store.ipynb
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[] | null | 1 | 2023-08-20T08:45:05 | 2023-08-20T22:59:40 | 2023-08-20T22:59:40 |
CONTRIBUTOR
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PR_kwDOIPDwls5YUTD4
| 9,507 |
Added In-Depth Langchain Agent Execution Guide
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CONTRIBUTOR
| null |
Made the notion document of how Langchain executes agents method by method in the codebase.
Can be helpful for developers that just started working with the Langchain codebase.
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how to improve the perfromance of agents to get better responses from the local model like gpt4all
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how to improve the perfromance of agents to get better responses from the local model like gpt4all
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QAGenerationChain.run often ends in JSONDecodeError
|
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[] | null | 5 | 2023-08-20T06:01:56 | 2023-10-03T13:43:01 | null |
CONTRIBUTOR
| null |
The `QAGenerationChain` as it is currently written is prone to a `JSONDecodeError`, as mentioned in https://github.com/langchain-ai/langchain/pull/9503. That was my naive attempt to fix the problem I was having, but as I explained in closing the PR, I think a `PydanticOutputParser` with a more instructive prompt or an auto-fixing parser would be more robust. Plus, I think the successful runs after implementing my fix were just luck. 🤣
In `QAGenerationChain._call`, after generation, `json.loads` frequently raises a `JSONDecodeError`. This is usually because the response is wrapped in Markdown code tags or prefaced with a message, as seen in the following traces:
- `ChatOpenAI` gpt-3.5-turbo: [successful trace](https://smith.langchain.com/public/6c42845c-bbe2-41c1-99a4-7669112f1504/r)
- `ChatOpenAI` gpt-4: [`JSONDecodeError` due to Markdown formatting](https://smith.langchain.com/public/d235ef1e-fb13-4177-adb3-6ddaf159bfea/r)
- `ChatAnthropic` Claude 2: [`JSONDecodeError` due to message before the JSON + Markdown formatting](https://smith.langchain.com/public/27eed20e-9ca5-4b4a-a3d3-71efde7aee3c/r)
- `ChatAnyscale` llama 2 70b: [successful trace](https://smith.langchain.com/public/e8f0b44c-424f-47ef-8cb3-31713b548d46/r?tab=0)
- `ChatAnyscale` llama 2 70b: [`JSONDecodeError` due to message before the JSON + Markdown formatting](https://smith.langchain.com/public/c89a7415-477d-4b82-a0a4-0a333b8b5680/r)
Each of these models had a mix of successful and failed runs. I probably should have lowered the temperature and tried non-chat models as well.
I would like to help however I could on this, I just want to be sure my fix aligns with the greater vision for this chain since it seems particularly useful. :)
### System Info
Version info from LangSmith:
```
RUNTIME
langchain_version: "0.0.268"
library: "langchain"
library_version: "0.0.268"
platform: "Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.36"
runtime: "python"
runtime_version: "3.11.4"
sdk_version: "0.0.25"
```
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.chains import QAGenerationChain
# tested with gpt-3.5-turbo, gpt-4, claude 2, and llama 2 70b
qagen = QAGenerationChain.from_llm(llm=llm)
question = qagen.run(docs[0].page_content)
# JSONDecodeError (sometimes)
```
The error is inconsistent, as seen in the screenshot above. This is for reasons we're all familiar with: perhaps the output is wrapped in Markdown code formatting with triple-backticks, maybe it's prefaced with a "sure, here's your JSON", etc.
Relevant examples from LangSmith:
- `ChatOpenAI` gpt-3.5-turbo: [successful trace](https://smith.langchain.com/public/6c42845c-bbe2-41c1-99a4-7669112f1504/r)
- `ChatOpenAI` gpt-4: [`JSONDecodeError` due to Markdown formatting](https://smith.langchain.com/public/d235ef1e-fb13-4177-adb3-6ddaf159bfea/r)
- `ChatAnthropic` Claude 2: [`JSONDecodeError` due to message before the JSON + Markdown formatting](https://smith.langchain.com/public/27eed20e-9ca5-4b4a-a3d3-71efde7aee3c/r)
- `ChatAnyscale` llama 2 70b: [successful trace](https://smith.langchain.com/public/e8f0b44c-424f-47ef-8cb3-31713b548d46/r?tab=0)
- `ChatAnyscale` llama 2 70b: [`JSONDecodeError` due to message before the JSON + Markdown formatting](https://smith.langchain.com/public/c89a7415-477d-4b82-a0a4-0a333b8b5680/r)
### Expected behavior
`QAGenerationChain.run` should return its expected output with no `JSONDecodeError`. I realize there's only so much we can do for the chain's default settings, and an LLM call can be inherently predictable, but some parsing and error handling would be nice. :)
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| 1,857,958,938 |
I_kwDOIPDwls5uvjQa
| 9,504 |
LLM Chain: Unable to run OpenLM - unexpected keyword argument 'api_key' in Completion.create class
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closed
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[] | null | 6 | 2023-08-20T05:26:12 | 2023-11-27T16:07:11 | 2023-11-27T16:07:11 |
NONE
| null |
### System Info
! pip show langchain
Name: langchain
Version: 0.0.268
Summary: Building applications with LLMs through composability
Home-page: https://www.github.com/hwchase17/langchain
! pip show openlm
Name: openlm
Version: 0.0.5
Summary: Drop-in OpenAI-compatible that can call LLMs from other providers
Python 3.11.3
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`
! pip install openlm
! pip install langchain
from getpass import getpass
import os
from langchain.llms import OpenLM
from langchain import PromptTemplate, LLMChain
os.environ['OPENAI_API_KEY'] = "<openai-api-key>"
os.environ['HF_API_TOKEN'] = "<hf-api-key>"
if "OPENAI_API_KEY" not in os.environ:
print("Enter your OpenAI API key:")
os.environ["OPENAI_API_KEY"] = getpass()
if "HF_API_TOKEN" not in os.environ:
print("Enter your HuggingFace Hub API key:")
os.environ["HF_API_TOKEN"] = getpass()
question = "What is the capital of France?"
template = """Question: {question} Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
for model in ["text-davinci-003", "huggingface.co/gpt2"]:
llm = OpenLM(model=model)
llm_chain = LLMChain(prompt=prompt, llm=llm)
result = llm_chain.run(question)
print( """Model: {} Result: {}""".format(model, result))
`
When I run the above code, I get the following error:
TypeError: Completion.create() got an unexpected keyword argument 'api_key'
Can I anyone help me to fix this issue ?
### Expected behavior
As mentioned the below document,
https://python.langchain.com/docs/integrations/llms/openlm
Model: text-davinci-003
Result: France is a country in Europe. The capital of France is Paris.
Model: huggingface.co/gpt2
Result: Question: What is the capital of France?
Answer: Let's think step by step. I am not going to lie, this is a complicated issue, and I don't see any solutions to all this, but it is still far more
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PR_kwDOIPDwls5YUA5y
| 9,503 |
Make QAGenerationChain.from_llm use SimpleJsonOutputParser
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closed
| false | null |
[] | null | 1 | 2023-08-20T04:18:03 | 2023-08-20T04:57:02 | 2023-08-20T04:57:02 |
CONTRIBUTOR
| null |
- Description: Gives `QAGenerationChain.from_llm` an `output_parser` keyword argument; adds default `SimpleJsonOutputParser`
- Issue: Default prompts request structured json, but no output parser is used. In `QAGenerationChain._call`, after generation, `json.loads` raises a `JSONDecodeError`.
Edit: It occurs to me that perhaps this should be a `PydanticOutputParser` or similar, and in fact I'm not sure it solves the original problem. :( Closing this for now, but open to discussion.
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| 1,857,932,992 |
I_kwDOIPDwls5uvc7A
| 9,502 |
Issue: Can't import HuggingFacePipeline
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[] | null | 10 | 2023-08-20T02:53:24 | 2024-01-04T12:54:06 | null |
NONE
| null |
> from langchain.llms import HuggingFacePipeline
`ImportError: cannot import name 'HuggingFacePipeline' from 'langchain.llms'`
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PR_kwDOIPDwls5YT8nT
| 9,501 |
Fast linters go first
|
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[] | null | 1 | 2023-08-20T01:49:01 | 2023-08-21T07:20:55 | 2023-08-21T07:20:55 |
COLLABORATOR
| null |
Proposal to reverse the order of linters based on the principle of running the
fast ones first.
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PR_kwDOIPDwls5YTzlm
| 9,500 |
Fixed issue with metadata in query
|
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[] | null | 4 | 2023-08-19T21:04:33 | 2023-08-20T23:00:14 | 2023-08-20T23:00:14 |
CONTRIBUTOR
| null |
- Description: Changed metadata retrieval so that it combines Vectara doc level and part level metadata
- Tag maintainer: @rlancemartin
- Twitter handle: @ofermend
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PR_kwDOIPDwls5YTyEz
| 9,499 |
fix typo in huggingface_hub.ipynb
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HuggingFaceInstructEmbeddings hangs in Docker container, but runs fine on MacOS
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[] | null | 8 | 2023-08-19T20:07:57 | 2023-11-23T01:51:04 | null |
NONE
| null |
### System Info
requirements.txt:
```
langchain==0.0.254
atlassian-python-api==3.36.0
chromadb==0.3.25
huggingface-hub==0.16.4
torch==2.0.1
sentence-transformers==2.2.2
InstructorEmbedding==1.0.0
p4python==2023.1.2454917
lxml==4.9.2
bs4==0.0.1
```
Dockerfile
```Dockerfile
FROM python:3.10
# Create a directory for your application
WORKDIR /app
COPY requirements.txt .
# Upgrade pip
RUN wget https://bootstrap.pypa.io/get-pip.py
RUN python3.10 get-pip.py
RUN python3.10 -m pip install --upgrade pip
RUN python3.10 -m pip install -r requirements.txt
# RUN python3.10 preload.py
RUN python3.10 -m pip install openai tiktoken
RUN apt-get update && apt-get install -y vim
COPY . .
ENTRYPOINT sleep infinity;
```
Host where I am running Docker: (Am also running python3.10 on this)
```bash
Software:
System Software Overview:
System Version: macOS 13.4 (22F66)
Kernel Version: Darwin 22.5.0
Boot Volume: Macintosh HD
Boot Mode: Normal
Hardware:
Hardware Overview:
Model Name: MacBook Pro
Model Identifier: Mac14,10
Chip: Apple M2 Pro
Total Number of Cores: 12 (8 performance and 4 efficiency)
Memory: 32 GB
```
### Who can help?
@agola11 @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run this script with above requirements.txt in docker container set up by Dockerfile
```python
from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings
from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import ConfluenceLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
loader = ConfluenceLoader(...)
documents = loader.load(...)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = HuggingFaceInstructEmbeddings(model_name= "hkunlp/instructor-large", model_kwargs={"device": "cpu"})
#Vector Database Storage: Store the generated embeddings in a vector database, allowing for efficient similarity searches.
db = Chroma.from_documents(texts, embeddings) # Stuck here
```
Successful runs where I am able to upload embeddings to db:
- Running this on my mac (no Docker)
- Running this in Docker container with OpenAIEmbeddings
Runs that get stuck:
- Running this in Docker container with HuggingFaceEmbeddings or HuggingFaceInstructEmbeddings
When I manually interrupt that stuck process, here is the traceback:
```bash
raceback (most recent call last):
File "/app/context.py", line 26, in <module>
ctx_manager.register_documents(paths, text_splitter_type, text_splitter_params,
File "/app/context_manager/context_manager/context_manager.py", line 197, in register_documents
self._vectordb_manager.add_documents(texts, embeddings,
File "/app/context_manager/db_managers/db_managers.py", line 92, in add_documents
vectorstore = Chroma.from_documents(
File "/usr/local/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 603, in from_documents
return cls.from_texts(
File "/usr/local/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 567, in from_texts
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
File "/usr/local/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 187, in add_texts
embeddings = self._embedding_function.embed_documents(texts)
File "/usr/local/lib/python3.10/site-packages/langchain/embeddings/huggingface.py", line 77, in embed_documents
embeddings = self.client.encode(texts, **self.encode_kwargs)
File "/usr/local/lib/python3.10/site-packages/sentence_transformers/SentenceTransformer.py", line 165, in encode
out_features = self.forward(features)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/container.py", line 217, in forward
input = module(input)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/sentence_transformers/models/Transformer.py", line 74, in forward
output_states = self.auto_model(**trans_features, return_dict=False)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1935, in forward
encoder_outputs = self.encoder(
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1094, in forward
layer_outputs = layer_module(
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 754, in forward
hidden_states = self.layer[-1](hidden_states)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 343, in forward
forwarded_states = self.DenseReluDense(forwarded_states)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 288, in forward
hidden_states = self.wi(hidden_states)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
KeyboardInterrupt
```
I thought it may be the CPU usage, but on my Mac, the CPU usage maxes out at around 270% and does not get stuck. However, in Docker, the CPU usage maxes out to 600% (reserved 6 CPUs), and still gets stuck. Memory is also lower than the limit in both environements.
### Expected behavior
Texts are supposed to get encoded then uploaded to vectorDB
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PR_kwDOIPDwls5YTt8G
| 9,497 |
Added misssing model name to GPT4AllEmbeddings.
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NONE
| null |
- Description: Added model name to `GPT4AllEmbeddings`. Apparently, when we use `CacheBackedEmbeddings`, we must provide a namespace so our doc embeddings with different model ids remain distinct. This change makes it easy to add name from the model.
https://python.langchain.com/docs/modules/data_connection/caching_embeddings
- Dependencies: None
- Tag maintainer: @baskaryan
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Update pydantic combat guide
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[] | null | 1 | 2023-08-19T18:37:33 | 2023-08-19T21:44:20 | 2023-08-19T21:44:19 |
COLLABORATOR
| null | null |
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| 1,857,809,616 |
PR_kwDOIPDwls5YTmVd
| 9,494 |
:bug: fix unexpected run of release workflow
|
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CONTRIBUTOR
| null |
I have discovered a bug located within `.github/workflows/_release.yml` which is the primary cause of continuous integration (CI) errors. The problem can be solved; therefore, I have constructed a PR to address the issue.
## The Issue
Access the following link to view the exact errors: [Langhain Release Workflow](https://github.com/langchain-ai/langchain/actions/workflows/langchain_release.yml)
The instances of these errors take place for **each PR** that updates `pyproject.toml`, excluding those specifically associated with bumping PRs.
See below for the specific error message:
```
Error: Error 422: Validation Failed: {"resource":"Release","code":"already_exists","field":"tag_name"}
```
An image of the error can be viewed here:
The `_release.yml` document contains the following if-condition:
```yaml
if: |
${{ github.event.pull_request.merged == true }}
&& ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
```
## The Root Cause
The above job constantly runs as the `if-condition` is always identified as `true`.
## The Logic
The `if-condition` can be defined as `if: ${{ b1 }} && ${{ b2 }}`, where `b1` and `b2` are boolean values. However, in terms of condition evaluation with GitHub Actions, `${{ false }}` is identified as a string value, thereby rendering it as truthy as per the [official documentation](https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idif).
I have run some tests regarding this behavior within my forked repository. You can consult my [debug PR](https://github.com/zawakin/langchain/pull/1) for reference.
Here is the result of the tests:
|If-Condition|Outcome|
|:--:|:--:|
|`if: true && ${{ false }}`|Execution|
|`if: ${{ false }}` |Skipped|
|`if: true && false` |Skipped|
|`if: false`|Skipped|
|`if: ${{ true && false }}` |Skipped|
In view of the first and second results, we can infer that `${{ false }}` can only be interpreted as `true` for conditions composed of some expressions.
It is consistent that the condition of `if: ${{ inputs.working-directory == 'libs/langchain' }}` works.
It is surprised to be skipped for the second case but it seems the spec of GitHub Actions 😓
Anyway, the PR would fix these errors, I believe 👍
Could you review this? @hwchase17 or @shoelsch , who is the author of [PR](https://github.com/langchain-ai/langchain/pull/360).
|
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| 1,857,805,597 |
PR_kwDOIPDwls5YTlhg
| 9,493 |
docs(readme): fixed badges with new github url
|
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[] | null | 2 | 2023-08-19T17:17:22 | 2023-08-19T21:51:39 | 2023-08-19T21:51:39 |
CONTRIBUTOR
| null |
Mainly created for the code space url that was broken but fixed the others in the same PR.
Now
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I_kwDOIPDwls5uu2cG
| 9,492 |
Issue: RetrievalQA runs twice faster than huggingface model call
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[] | null | 5 | 2023-08-19T15:26:52 | 2023-11-30T16:07:16 | 2023-11-30T16:07:15 |
NONE
| null |
### Issue you'd like to raise.
I have the following code:
```
pipe = transformers.pipeline(
model=llm_model,
tokenizer=tokenizer,
return_full_text=True,
task='text-generation',
temperature=0.2,
max_new_tokens=200
)
llm = HuggingFacePipeline(pipeline=pipe)
retriever = vector_db.as_retriever(search_kwargs={"k": 4})
qa = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True)
```
When I call qa(query) it executes in about 24 seconds. But when I call llm with the same query and context obtained from retriever.get_relevant_documents method it gives me different result (but still relevant) and executes in 39 seconds, almost twice as long as qa. So i would like to understand what happens under the hood when I call RetrievalQA and why results are so different in speed.
### Suggestion:
_No response_
|
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| 1,857,764,440 |
PR_kwDOIPDwls5YTdns
| 9,491 |
chatbots use case - fix broken collab URL
|
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[] | null | 2 | 2023-08-19T14:45:57 | 2023-08-19T21:53:54 | 2023-08-19T21:53:54 |
CONTRIBUTOR
| null |
The current Collab URL returns a 404, since there is no `chatbots` directory under `use_cases`.
<!-- Thank you for contributing to LangChain!
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5YTQNo
| 9,490 |
Fix typo in apis.ipynb
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[] | null | 1 | 2023-08-19T12:34:02 | 2023-08-19T13:26:08 | 2023-08-19T13:26:08 |
CONTRIBUTOR
| null |
funtions -> functions
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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I_kwDOIPDwls5uug89
| 9,489 |
Issue: RunnableWithFallbacks: Can't instantiate abstract class BaseLanguageModel ???
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NONE
| null |
### Issue you'd like to raise.
I bumped langchain to version 0.0.268 and encountered below error while implementing 'with_fallbacks'.
Error:
pydantic.error_wrappers.ValidationError: 1 validation error for LLMChain
llm
Can't instantiate abstract class BaseLanguageModel with abstract methods agenerate_prompt, apredict, apredict_messages, generate_prompt, invoke, predict, predict_messages (type=type_error)
Sample Code:
chat_llm = ChatOpenAI(model_name='gpt-3.5-turbo',
temperature=0,
model_kwargs={'top_p': 0.95},
max_tokens=512,
streaming=False,
verbose=False
)
fallback_llm = ChatOpenAI(model_name='gpt-4',
temperature=0,
model_kwargs={'top_p': 0.95},
max_tokens=512,
streaming=False,
verbose=False
)
chat_llm.with_fallbacks([fallback_llm], exceptions_to_handle=(Exception,))
Pydantic version: 1.10.12
Is this because of pydantic migration? Any immediate fixes or should i wait till Aug 25th for the migration to complete?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5uuPgz
| 9,488 |
[ask]: about baiduwenxin LLM
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[] | null | 2 | 2023-08-19T07:19:40 | 2023-11-25T16:06:48 | 2023-11-25T16:06:48 |
NONE
| null |
### Issue you'd like to raise.
May I ask if there are any plans to support baiduwenxin llm in the near future? I saw that the JavaScript version has baiduwenxin, but Python does not
### Suggestion:
_No response_
|
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| 1,857,558,456 |
PR_kwDOIPDwls5YS0Q9
| 9,487 |
bump 269
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[] | null | 1 | 2023-08-19T04:38:53 | 2023-08-21T15:34:17 | 2023-08-21T15:34:16 |
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I_kwDOIPDwls5ut4zL
| 9,486 |
Problem with embedding model
|
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[] | null | 12 | 2023-08-19T03:25:59 | 2023-12-02T16:06:12 | 2023-12-02T16:06:11 |
NONE
| null |
### Issue you'd like to raise.
I use embedding model from huggingface vinai/phobert-base:
Then it has this problem:
WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name /root/.cache/torch/sentence_transformers/vinai_phobert-base. Creating a new one with MEAN pooling.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
### Suggestion:
_No response_
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PR_kwDOIPDwls5YSehK
| 9,485 |
Bagatur/promptguard
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PR_kwDOIPDwls5YSVD2
| 9,484 |
Let you specify run id in trace as chain group
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closed
| false | null |
[] | null | 1 | 2023-08-19T00:16:53 | 2023-08-19T00:21:54 | 2023-08-19T00:21:53 |
COLLABORATOR
| null |
I think we'll deprecate this soon anyway but still nice to be able to fetch the run id
|
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https://api.github.com/repos/langchain-ai/langchain/issues/9484/timeline
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