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https://api.github.com/repos/langchain-ai/langchain/issues/8936
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| 1,842,127,127 |
I_kwDOIPDwls5tzKEX
| 8,936 |
DOC: API Reference: bug in the Search
|
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[] | null | 1 | 2023-08-08T21:28:15 | 2023-11-14T16:05:11 | 2023-11-14T16:05:10 |
COLLABORATOR
| null |
### Issue with current documentation:
Try https://api.python.langchain.com/en/latest/search.html?q=PubMed (a search on the "PubMed" string)
It returns too many lines.
It seems it converts every found "PubMed" string into a link. That creates duplicate links in big quantities.
### Idea or request for content:
Fix a script that generates the "Search results" page.
|
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PR_kwDOIPDwls5XeA4A
| 8,932 |
bump 258
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[] | null | 1 | 2023-08-08T19:44:15 | 2023-08-08T19:54:52 | 2023-08-08T19:54:51 |
COLLABORATOR
| null | null |
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| 1,841,912,446 |
I_kwDOIPDwls5tyVp-
| 8,931 |
Local huggingface model for summarization task
|
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[] | null | 4 | 2023-08-08T19:23:10 | 2023-12-25T16:02:42 | null |
NONE
| null |
### Issue you'd like to raise.
I was using Langchain (map reduce) for summarization of longer documents with local HuggingFace model. I am using local models as my work prohibits me to directly connect to huggingface and/or openai.
I am having some issue in running the model. I am getting the following errors:
"'HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /gpt2/resolve/main/tokenizer_config.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f11d2f511d0>, 'Connection to huggingface.co timed out. (connect timeout=10)'))' thrown while requesting HEAD https://huggingface.co/gpt2/resolve/main/tokenizer_config.json"
My code is:
`from transformers import AutoTokenizer, LlamaForCausalLM,AutoModelForSeq2SeqLM,AutoModel, AutoModelForCausalLM, , LlamaTokenizer, LlamaForCausalLM,AutoModelForCausalLM,pipeline
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
from langchain.docstore.document import Document
from langchain.document_loaders import PyPDFLoader
import os
import torch
model = AutoModelForSeq2SeqLM.from_pretrained("/home/projects/nlp_summarize/led-large-16384")
tokenizer2 = AutoTokenizer.from_pretrained("/home/projects/nlp_summarize/led-large-16384")
tokenizer2.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True
pipe = pipeline(
"text2text-generation",
model=model,
tokenizer=tokenizer2,
#max_new_tokens= 4096,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.15,device= DEVICE
)
llm = HuggingFacePipeline(pipeline=pipe)
text_splitter = CharacterTextSplitter()
with open("stateoftheunion.txt") as f:
data = f.read()
texts = text_splitter.split_text(data)
docs = [Document(page_content=t) for t in texts[:]]
chain = load_summarize_chain(llm, chain_type="map_reduce")
output_summary = chain.run(docs)
`
Even though I am not using any gpt2 model2 but still the model is looking for a gpt2 model online.
Any idea why such behavior is happening and how can I avoid it?
### Suggestion:
_No response_
|
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| 1,841,811,758 |
I_kwDOIPDwls5tx9Eu
| 8,930 |
Qdrant support for async embedding functions in aadd_texts
|
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[] | null | 1 | 2023-08-08T18:27:50 | 2023-11-14T16:06:19 | 2023-11-14T16:06:18 |
CONTRIBUTOR
| null |
### Feature request
The qdrant vector store currently has an async function for adding texts to the (aadd_texts) which only supports creating embeddings with the _generate_rest_batches function synchronously.
There could be the option to add a async embedding function and aadd_texts could maybe then have a parameter to create the embeddings async with an async version of _generate_rest_batches.
### Motivation
It would be nice to have the option to have an aadd_texts function with completely non blocking IO.
### Your contribution
I could try to implement this or support with the implementation.
|
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| 1,841,767,814 |
I_kwDOIPDwls5txyWG
| 8,928 |
n hyperparameter in AzureOpenai is not updated
|
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[] | null | 4 | 2023-08-08T18:03:51 | 2023-11-29T09:49:10 | 2023-11-29T09:49:10 |
CONTRIBUTOR
| null |
### System Info
langchain==0.0.257
Python 3.8.17
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.llms.openai import AzureOpenAI
llm = AzureOpenAI(deployment_name="text-davinci-003", verbose=True)
llm._generate(["You are a chatbot. \nUser: How are you?\nBot:",
"You are a chatbot. \nUser: What is the weather like?\nBot:"], n=2)
```
### Expected behavior
The result should be the same when we are providing 'n' parameter when initiating the 'AzureOpenAI' class in the beginning. However, it is not being handled, when passing it through the 'generate' function.
When n is "2", Generations only contain 1 example for each prompt, however, I expect it to have 2 examples for each. Additionally, 2 responses for the first prompt are returned as the result of two prompts, ultimately losing the result of the second prompt.
result:
```
[
[Generation(text=" I'm doing great, thanks for asking! How about you?", generation_info={'finish_reason': 'stop', 'logprobs': None
})
],
[Generation(text=" I'm doing well, thanks for asking! How about you?", generation_info={'finish_reason': 'stop', 'logprobs': None
})
]
]
```
This may be because when the 'create_llm_result' function is called, it does not receive the recent value of 'n', instead of 2 it shows 'n' as 1. When I manually set 'self.n' to 2, it returns the expected result.
code change:
```
def create_llm_result(
self, choices: Any, prompts: List[str], token_usage: Dict[str, int]
) -> LLMResult:
"""Create the LLMResult from the choices and prompts."""
generations = []
++ self.n = 2
for i, _ in enumerate(prompts):
sub_choices = choices[i * self.n : (i + 1) * self.n]
```
result:
```
[
[Generation(text=" I'm doing great, thanks for asking! How can I help you today?", generation_info={'finish_reason': 'stop', 'logprobs': None
}), Generation(text=" I'm doing well, thank you for asking! How can I help you?", generation_info={'finish_reason': 'stop', 'logprobs': None
})
],
[Generation(text=' The weather today is mostly sunny with a high of 70 degrees Fahrenheit and a low of 45 degrees Fahrenheit.', generation_info={'finish_reason': 'stop', 'logprobs': None
}), Generation(text=' The current weather is sunny and warm with temperatures in the high 70s.', generation_info={'finish_reason': 'stop', 'logprobs': None
})
]
]
```
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nit
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PR_kwDOIPDwls5XdDfF
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Remove Evaluation from Modules page
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[] | null | 3 | 2023-08-08T17:07:46 | 2023-08-08T17:20:25 | 2023-08-08T17:20:25 |
CONTRIBUTOR
| null |
Remove Evaluation link (which gives 404 now) from Modules page, since it lives under Guides page now
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PR_kwDOIPDwls5XdAGq
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Fix typo in Vectara docs
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[] | null | 2 | 2023-08-08T16:55:35 | 2023-08-08T17:11:07 | 2023-08-08T17:11:07 |
CONTRIBUTOR
| null |
Fixed a typo in the Vectara docs description.
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- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
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PR_kwDOIPDwls5Xc4ad
| 8,924 |
Automatically set docs appearance to system default
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[] | null | 2 | 2023-08-08T16:33:54 | 2023-08-08T16:54:19 | 2023-08-08T16:54:19 |
CONTRIBUTOR
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@baskaryan
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PR_kwDOIPDwls5Xc3ok
| 8,923 |
respect docs light pref
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[] | null | 1 | 2023-08-08T16:31:19 | 2023-08-08T16:54:06 | 2023-08-08T16:54:06 |
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PR_kwDOIPDwls5XceNK
| 8,920 |
Fix Pinecone cosine relevance score
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[] | null | 4 | 2023-08-08T15:19:13 | 2023-11-13T19:47:38 | 2023-11-13T19:47:38 |
CONTRIBUTOR
| null |
Fixes: #8207
Description:
Pinecone returns scores (not distances) with cosine similarity. The values according to the docs are [-1, 1], although I could never reproduce negative values.
This PR ensures that the score returned from Pinecone is preserved, rather than inverted, so the most relevant documents can be filtered (eg when using similarity thresholds)
I'll leave this as a draft PR as I couldn't run the tests (my pinecone account might not be enough - some errors were being thrown around namespaces) so hopefully someone who _can_ will pick this up.
Maintainers:
@rlancemartin, @eyurtsev
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| 1,841,523,033 |
PR_kwDOIPDwls5Xcd_1
| 8,919 |
feat: helper function to filter metadata for Chroma
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[] | null | 6 | 2023-08-08T15:18:36 | 2023-08-11T08:11:08 | 2023-08-11T08:11:08 |
CONTRIBUTOR
| null |
### Summary
Adds a utility function to filter complex (i.e. list, dict) metadata from documents. This is helpful because some vector dbs such as Chroma do not support loading list metadata. Currently, if your `Document` has a metadata item that's a list, you get the following when you run `Chroma.from_documents`.
```python
File ~/.pyenv/versions/chromadb/lib/python3.10/site-packages/chromadb/api/types.py:140, in validate_metadata(metadata)
138 # isinstance(True, int) evaluates to True, so we need to check for bools separately
139 if not isinstance(value, bool) and not isinstance(value, (str, int, float)):
--> 140 raise ValueError(
141 f"Expected metadata value to be a str, int, float or bool, got {value} which is a {type(value)}"
142 )
143 return metadata
ValueError: Expected metadata value to be a str, int, float or bool, got [{'text': 'CNN', 'url': '/'}] which is a <class 'list'>
```
The helper function operates on `Document` objects and you can call it like this.
```python
from langchain.vectorstore.utils import filter_complex_metadata
updated_documents = filter_complex_metadata(documents)
```
### Testing
Confirmed the notebook in [this `unstructured` PR](https://github.com/Unstructured-IO/unstructured/pull/1054) runs on the feature branch, where previously it failed. Additionally, run the following to execute the unit test:
`pytest tests/unit_tests/vectorstores/test_utils.py`
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
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PR_kwDOIPDwls5XcUld
| 8,918 |
Fixes to the Nebula LLM Integration
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[] | null | 3 | 2023-08-08T14:54:23 | 2023-08-08T17:04:43 | 2023-08-08T17:04:43 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
This addresses some issues with introducing the Nebula LLM to LangChain in this PR:
https://github.com/langchain-ai/langchain/pull/8876
This fixes the following:
- Removes `SYMBLAI` from variable names
- Fixes bug with `Bearer` for the API KEY
Thanks again in advance for your help!
cc: @hwchase17, @baskaryan
|
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I_kwDOIPDwls5twgRH
| 8,917 |
SearchFieldDataType.Single raises error in azuresearch.py, under AzureSearch() class.
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[] | null | 4 | 2023-08-08T14:27:07 | 2023-08-11T14:08:16 | 2023-08-08T21:08:04 |
NONE
| null |
### System Info
Platform: MacOS
Python version: Python 3.10.12
LangChain version: 0.0.257
Azure Search package version: 1.0.0b2
Azure Search Document package version: 11.3.0
azure-search==1.0.0b2
azure-search-documents==11.3.0
langchain==0.0.257
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. pip install packages langchain, azure-search, azure-search-documents.
2. Try to create an instance of AzureSearch as described [here](https://python.langchain.com/docs/integrations/vectorstores/azuresearch). (Official LangChain documentation.)
3. Receive error `AttributeError: module ‘azure.search.documents.indexes.models._edm’ has no attribute ‘Single’` from:
```
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query,
)
```
### Expected behavior
AzureSearch instance should be created without any errors from langchain dependencies. _edm.py file in azure-search-documents package does not define Single as a data type.
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PR_kwDOIPDwls5XcCuu
| 8,916 |
Fix imports in awslambda docs
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[] | null | 2 | 2023-08-08T14:08:50 | 2023-08-08T15:29:29 | 2023-08-08T15:29:29 |
CONTRIBUTOR
| null |
Minor doc fix to awslambda tool notebook.
Add missing import for initialize_agent to awslambda agent example
|
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| 8,915 |
Fixed inaccurate import in integrations:providers:bedrock documentation
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[] | null | 2 | 2023-08-08T13:35:38 | 2023-08-08T14:24:37 | 2023-08-08T14:24:37 |
CONTRIBUTOR
| null |
Description:
Fixed inaccurate import in integrations:providers:bedrock documentation
In the current version of the bedrock documentation, page https://python.langchain.com/docs/integrations/providers/bedrock it states that the import is from langchain import Bedrock
This has been changed to from langchain.llms.bedrock import Bedrock as stated in https://python.langchain.com/docs/integrations/llms/bedrock
Issue:
Not applicable
Dependencies
No dependencies required
Tag maintainer
@baskaryan
Twitter handle:
Not applicable
|
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PR_kwDOIPDwls5Xbklr
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feat(integration): Add support to serialize protobufs in WandbTracer
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[] | null | 4 | 2023-08-08T12:53:04 | 2023-08-15T08:05:12 | 2023-08-15T08:05:12 |
CONTRIBUTOR
| null |
This PR adds serialization support for protocol bufferes in `WandbTracer`. This allows code generation chains to be visualized. Additionally, it also fixes a minor bug where the settings are not honored when a run is initialized before using the `WandbTracer`
@agola11
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I_kwDOIPDwls5tvs1r
| 8,913 |
running HuggingFaceTextGenInference from an agent gives token limit warning
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[] | null | 5 | 2023-08-08T12:32:10 | 2023-12-28T16:02:32 | null |
NONE
| null |
### System Info
LangChain version - 0.0.257
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
i run the following
```
llm = HuggingFaceTextGenInference(
inference_server_url="http://" + settings.self_hosted_server_ip+":8080",
max_new_tokens=2000,
top_k=5,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.23,
callbacks = callbacks,
stream = True
)
my_chain = LLMChain(
llm=llm, # type: ignore
prompt=prompt,
verbose=False,
)
tools = [PythonAstREPLTool()]
custom_agent = LLMSingleActionAgent(
llm_chain=my_chain,
output_parser=my_output_parser,
stop=["\nObservation:"],
)
agent_memory = CustomConversationTokenBufferMemory(
k=1,
llm=llm, # type: ignore
max_token_limit=4096,
memory_key="history"
)
return AgentExecutor.from_agent_and_tools(
agent=custom_agent,
tools=tools,
verbose=True,
memory=agent_memory,
)
```
where the HF inference endpoint is running with the following docker :
```
model=meta-llama/Llama-2-70b-chat-hf
docker run -d --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --max-input-length 3000 --max-total-tokens 4096 --quantize bitsandbytes --json-output --model-id $model --trust-remote-code
```
the error i get is :
**Token indices sequence length is longer than the specified maximum sequence length for this model (1286 > 1024). Running this sequence through the model will result in indexing errors**
but the Llama-2-70B model token limit is 4096....
i tried to test using just the :
```
llm_local = HuggingFaceTextGenInference(
inference_server_url=inference_server_url_local,
max_new_tokens=2000,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.7,
repetition_penalty=1.03,
)
```
with :
```
llm_chain_local = LLMChain(prompt=prompt, llm=llm_local)
print(llm_chain_local("some query"))
```
and didnt get any error
### Expected behavior
i am not expecting any token limit issue
|
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| 1,841,204,854 |
PR_kwDOIPDwls5XbY09
| 8,912 |
Fixed a source for documents uploaded from GCS
|
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[] | null | 1 | 2023-08-08T12:22:11 | 2023-08-08T13:34:44 | 2023-08-08T13:34:43 |
CONTRIBUTOR
| null |
#8911
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| 1,841,196,684 |
I_kwDOIPDwls5tvm6M
| 8,911 |
GCSFileLoader stores a wrong source in the metadata
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[] | null | 2 | 2023-08-08T12:17:01 | 2023-08-08T21:23:10 | 2023-08-08T21:23:10 |
CONTRIBUTOR
| null |
### System Info
`0.0.257`
### 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
Load a document from GCS and check the metadata["source"], it would point to the file in `/tmp` directory
### Expected behavior
source pointing to an original file on GCS
|
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PR_kwDOIPDwls5XbLFX
| 8,910 |
addition to docs at 'Store and reference chat history'
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[] | null | 3 | 2023-08-08T11:41:31 | 2023-08-08T17:10:17 | 2023-08-08T17:10:11 |
CONTRIBUTOR
| null |
- Description: I have added an example showing how to pass a custom template to ConversationRetrievalChain. Instead of CONDENSE_QUESTION_PROMPT we can pass any prompt in the argument condense_question_prompt. Look in Use cases -> QA over Documents -> How to -> Store and reference chat history,
- Issue: #8864,
- Dependencies: NA,
- Tag maintainer: @hinthornw,
- Twitter handle:
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| 8,909 |
add tests to trajectory eval chain
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[] | null | 2 | 2023-08-08T11:04:35 | 2023-08-09T14:57:18 | 2023-08-09T14:57:18 |
CONTRIBUTOR
| null |
# What
- add tests to trajectory eval chain
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add tests to trajectory eval chain
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle:@MLOpsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,841,082,252 |
PR_kwDOIPDwls5Xa-OB
| 8,908 |
Improve query prompt of BGE embeddings
|
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[] | null | 1 | 2023-08-08T11:01:20 | 2023-08-08T17:20:38 | 2023-08-08T17:20:38 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: Improved query of BGE embeddings after talking with the devs of BGE embeddings ,
- Dependencies: any dependencies required for this change,
- Tag maintainer: @hwchase17 ,
- Twitter handle: @ManabChetia3
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I_kwDOIPDwls5tvJsk
| 8,907 |
RdfGraph schema retrieval queries for the relation types are not linked by the correct comment variable
|
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[] | null | 1 | 2023-08-08T10:57:54 | 2023-10-25T20:36:59 | 2023-10-25T20:36:59 |
NONE
| null |
### System Info
langchain = 0.0.251
Python = 3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OWL ontology called `dbpedia_sample.ttl` with the following:
``` turtle
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix wikidata: <http://www.wikidata.org/entity/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix : <http://dbpedia.org/ontology/> .
:Actor
a owl:Class ;
rdfs:comment "An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity."@en ;
rdfs:label "actor"@en ;
rdfs:subClassOf :Artist ;
owl:equivalentClass wikidata:Q33999 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:Actor> .
:AdministrativeRegion
a owl:Class ;
rdfs:comment "A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)"@en ;
rdfs:label "administrative region"@en ;
rdfs:subClassOf :Region ;
owl:equivalentClass <http://schema.org/AdministrativeArea>, wikidata:Q3455524 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyClass:AdministrativeRegion> .
:birthPlace
a rdf:Property, owl:ObjectProperty ;
rdfs:comment "where the person was born"@en ;
rdfs:domain :Animal ;
rdfs:label "birth place"@en ;
rdfs:range :Place ;
rdfs:subPropertyOf dul:hasLocation ;
owl:equivalentProperty <http://schema.org/birthPlace>, wikidata:P19 ;
prov:wasDerivedFrom <http://mappings.dbpedia.org/index.php/OntologyProperty:birthPlace> .
```
2. Run
``` python
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="dbpedia_sample.ttl",
serialization="ttl",
standard="owl"
)
print(graph.get_schema)
```
3. Output
```
In the following, each IRI is followed by the local name and optionally its description in parentheses.
The OWL graph supports the following node types:
<http://dbpedia.org/ontology/Actor> (Actor, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/AdministrativeRegion> (AdministrativeRegion, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration))
The OWL graph supports the following object properties, i.e., relationships between objects:
<http://dbpedia.org/ontology/birthPlace> (birthPlace, An actor or actress is a person who acts in a dramatic production and who works in film, television, theatre, or radio in that capacity.),
<http://dbpedia.org/ontology/birthPlace> (birthPlace, A PopulatedPlace under the jurisdiction of an administrative body. This body may administer either a whole region or one or more adjacent Settlements (town administration)), <http://dbpedia.org/ontology/birthPlace> (birthPlace, where the person was born)
The OWL graph supports the following data properties, i.e., relationships between objects and literals:
```
### Expected behavior
The issue is that in the SPARQL queries getting the properties the `rdfs:comment` triple pattern always refers to the variable `?cls` which obviously comes from copy/paste code.
For example, getting the RDFS properties via
``` python
rel_query_rdf = prefixes["rdfs"] + (
"""SELECT DISTINCT ?rel ?com\n"""
"""WHERE { \n"""
""" ?subj ?rel ?obj . \n"""
""" OPTIONAL { ?cls rdfs:comment ?com } \n"""
"""}"""
)
```
you can see that the `OPTIONAL` clause refers to `?cls`, but it should be `?rel`.
The same holds for all other queries regarding properties.
The current status leads to a cartesian product of properties and all `rdfs:comment` vlaues in the dataset, which can be horribly large and of course leads to misleading and huge prompts (see the output of my sample in the "reproduction" part)
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| 1,841,068,385 |
I_kwDOIPDwls5tvHlh
| 8,906 |
Cloud Firestore Chat Message History
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[] | null | 3 | 2023-08-08T10:52:17 | 2023-11-15T16:10:22 | 2023-11-15T16:10:21 |
NONE
| null |
### Feature request
Implement a Chat Message History class backed up by Google Cloud Firestore.
### Motivation
It's a common No-SQL database, used by a lot of people to build MVPs, due to its friendly pricing.
### Your contribution
I'd submit a PR to implement this, if you guys think that it could be a helpful feature
|
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| 1,840,945,541 |
I_kwDOIPDwls5tupmF
| 8,905 |
VectorStore: Add entries by providing embeddings and not an embedding function
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[] | null | 1 | 2023-08-08T09:35:08 | 2023-11-14T16:05:19 | 2023-11-14T16:05:18 |
NONE
| null |
### Feature request
I'm looking for a way to add entries to a vector store when the embeddings already exist and I don't want to calculate them again. However, it seems like this is not possible with langchain at the moment. Maybe something like an `add_entries()` function would be nice, where the data needs to be exactly in the form as it's stored in the DB.
### Motivation
What if you want to add entries but you already have embeddings? What if you need to calculate embeddings separately from the "add to db" step?
### Your contribution
I already built a workaround for my own problem but it's very hacky. It would be nice if langchain could do this natively.
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| 8,904 |
There is a bug in [Integration]-->[ChatGLM]
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[] | null | 2 | 2023-08-08T09:08:19 | 2023-11-14T16:05:28 | 2023-11-14T16:05:27 |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.256
platform: Linux
Python 3.9.16
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
There is a mistake in langchain.llms.chatglm.py
in line 128, where:
**`self.history = self.history + [[None, parsed_response["response"]]]`**
when you run this code, the log file shows like this:
2023-08-08 16:44:26,370 - ChatGLM - Query - 谢谢
2023-08-08 16:44:26,370 - ChatGLM - History - [['我将从美国到中国来旅游，出行前希望了解中国的城市', '欢迎问我任何问题。'], [None, '我是一个人工智能助手,我将尽力回答您的问题。'], [None, '我能回答各种问题、提供信息、帮助解决问题等。']]
Actually,it should be:
**`self.history = self.history + [[prompt, parsed_response["response"]]]`**
In this way, my input prompt can be recorded in the list correctly!
### Expected behavior
record my input prompt in a list with correspond response as history.
|
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PR_kwDOIPDwls5XZ_oi
| 8,903 |
bump 257
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[] | null | 1 | 2023-08-08T08:02:43 | 2023-08-08T08:16:34 | 2023-08-08T08:16:33 |
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PR_kwDOIPDwls5XZarU
| 8,902 |
Relax Validation in Eval
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[] | null | 1 | 2023-08-08T06:05:00 | 2023-08-08T18:59:31 | 2023-08-08T18:59:30 |
COLLABORATOR
| null |
Just check for missing keys
|
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| 1,840,608,946 |
I_kwDOIPDwls5ttXay
| 8,901 |
Retrievers inheriting from BaseRetriever are incompatible with typing.Protocol
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[] | null | 2 | 2023-08-08T05:50:30 | 2023-11-14T16:07:42 | 2023-11-14T16:07:41 |
CONTRIBUTOR
| null |
### System Info
langchain v0.0.254
python 3.11
### Who can help?
In this commit https://github.com/langchain-ai/langchain/commit/81e5b1ad362e9e6ec955b6a54776322af82050d0#diff-5d00673e4963a0b2c6bf091d22f98c30d267b20fea4d15d9541ba6d1f5d79e4fR20 inheritance from `Serializable` was introduced. This inheritance is the root of the problem. The commit was by @nfcampos. Perhaps you can help?
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Simply run this code:
``` python
from typing import Protocol
from langchain.schema.retriever import BaseRetriever
class Foo(Protocol):
bar: int
class IAmAFailure(BaseRetriever):
foo: Foo
```
You will then see the error:
```
Traceback (most recent call last):
File "/home/yolen/Desktop/langchain_bug.py", line 9, in <module>
class IAmAFailure(Serializable):
File "pydantic/main.py", line 197, in pydantic.main.ModelMetaclass.__new__
File "pydantic/fields.py", line 506, in pydantic.fields.ModelField.infer
File "pydantic/fields.py", line 436, in pydantic.fields.ModelField.__init__
File "pydantic/fields.py", line 552, in pydantic.fields.ModelField.prepare
File "pydantic/fields.py", line 639, in pydantic.fields.ModelField._type_analysis
File "/usr/local/lib/python3.11/typing.py", line 1960, in __instancecheck__
raise TypeError("Instance and class checks can only be used with"
TypeError: Instance and class checks can only be used with @runtime_checkable protocols
```
Adding the `@runtime_checkable` decorator does not help (just changes the error). To my understanding, this problem is cased by the inheritance of `Serializable` in the new release https://github.com/langchain-ai/langchain/blob/fa30a57034b6359e8de36bbb98766d2214acfcbd/libs/langchain/langchain/schema/retriever.py#L21.
### Expected behavior
I would expect that I can use Protocols with retrievers. In my use case, I, among others, inject a class that can compute a query embedding. The `QueryEmbedding` class is a protocol e.g.
```python
class QueryEmbedder(Protocol):
async def get_embeddings(self, *, text: str) -> list[TextEmbedding]:
...
class FooRetriever(BaseRetriever):
def __init__(self,*, query_embedder:QueryEmbedder)->None:
self.query_embedder=query_embedder
async def _aget_relevant_documents(
self, query: str,
*, run_manager: AsyncCallbackManagerForRetrieverRun) -> list[Document]:
query_embedding = await self.query_embedder.embed(query=query)
...
```
Protocols are very good at reducing the coupling in your code base. Furthermore, I do think that using multiple inheritance will cause problems at some stage. _Composition over inheritance_
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| 1,840,560,242 |
PR_kwDOIPDwls5XZNRG
| 8,900 |
fix(llms): on_llm_start could change prompts
|
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[] | null | 5 | 2023-08-08T04:51:50 | 2023-08-09T00:02:35 | 2023-08-09T00:02:35 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: on_llm_start could change prompts
- Issue: the issue #8725
- Tag maintainer: @baskaryan @agola11
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
- Description: on_llm_start could change prompts
- Issue: the issue #8725
- Tag maintainer: @baskaryan @agola11
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| 1,840,506,624 |
I_kwDOIPDwls5ts-cA
| 8,899 |
Type Mismatch For using OpenAI Interface
|
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[] | null | 4 | 2023-08-08T03:37:55 | 2023-08-08T10:56:43 | 2023-08-08T10:56:43 |
NONE
| null |
### System Info
I use OpenAIEmbeddings from langchain.embedding, and using openAI function.
However I find it's a problem that when we call OpenAI Interface, the input is a 2D list, not a 1D list.
If the input is a 1D list, it could work for embedding.create func
like this:
and I found out that openai api does not support 2D list as input.
how could I solve this problem
the error is shown below:
```
Traceback (most recent call last):
File "..\main.py", line 47, in <module>
langchain_openai()
File "..\main.py", line 42, in langchain_openai
query_result = embeddings.embed_query(text)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 501, in embed_query
return self.embed_documents([text])[0]
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 473, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 359, in _get_len_safe_embeddings
response = embed_with_retry(
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 108, in embed_with_retry
return _embed_with_retry(**kwargs)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\tenacity\__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\tenacity\__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\tenacity\__init__.py", line 314, in iter
return fut.result()
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\concurrent\futures\_base.py", line 437, in result
return self.__get_result()
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\concurrent\futures\_base.py", line 389, in __get_result
raise self._exception
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\tenacity\__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\langchain\embeddings\openai.py", line 105, in _embed_with_retry
response = embeddings.client.create(**kwargs)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_resources\embedding.py", line 33, in create
response = super().create(*args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_requestor.py", line 298, in request
resp, got_stream = self._interpret_response(result, stream)
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_requestor.py", line 700, in _interpret_response
self._interpret_response_line(
File "C:\ProgramData\Anaconda3\envs\python3.8\lib\site-packages\openai\api_requestor.py", line 763, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: parse parameter error: type mismatch
```
### Who can help?
@hwchase17 @agola11
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
def langchain_openai():
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
model="text-embedding-ada-002",
openai_api_base="",
openai_api_key=""
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
print(query_result[:5])
```
### Expected behavior
I think it's a bug or something to fix
|
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| 1,840,501,592 |
I_kwDOIPDwls5ts9NY
| 8,898 |
how to expand the integrated content
|
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[] | null | 5 | 2023-08-08T03:33:04 | 2023-11-14T16:05:37 | 2023-11-14T16:05:36 |
NONE
| null |
### Issue with current documentation:
Hello, how to expand the integrated content, such as the SQLDatabaseToolkit class, after executing the existing tools to continue other work, I checked the following code in the SQLDatabaseToolkit class,`
def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
return [
QuerySQLDataBaseTool(db=self.db),
InfoSQLDatabaseTool(db=self.db),
ListSQLDatabaseTool(db=self.db),
QueryCheckerTool(db=self.db, llm=self.llm),
]`
how can I add my own tool class to expand it?
I tried to subclass BaseTool according to the instructions in the documentation, and then override the get_tools method of the SQLDatabaseToolkit class to add my custom BaseTool. However, it failed.
I'm also considering how to extend the integration for other components to achieve my task scenario.
### Idea or request for content:
_No response_
|
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| 1,840,497,093 |
I_kwDOIPDwls5ts8HF
| 8,897 |
How to retrieve vectors by ids for LangChain vectorstore FAISS?
|
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[] | null | 6 | 2023-08-08T03:26:19 | 2023-08-09T08:15:18 | 2023-08-09T08:15:18 |
NONE
| null |
### Feature request
In Qdrant vectorstore, we have a method:
```
def retrieve(
self,
collection_name: str,
ids: Sequence[types.PointId],
with_payload: Union[bool, Sequence[str], types.PayloadSelector] = True,
with_vectors: Union[bool, Sequence[str]] = False,
consistency: Optional[types.ReadConsistency] = None,
) -> List[types.Record]:
"""Retrieve stored points by IDs
Args:
collection_name: Name of the collection to lookup in
ids: list of IDs to lookup
with_payload:
- Specify which stored payload should be attached to the result.
- If `True` - attach all payload
- If `False` - do not attach any payload
- If List of string - include only specified fields
- If `PayloadSelector` - use explicit rules
with_vectors:
- If `True` - Attach stored vector to the search result.
- If `False` - Do not attach vector.
- If List of string - Attach only specified vectors.
- Default: `False`
consistency:
Read consistency of the search. Defines how many replicas should be queried before returning the result.
Values:
- int - number of replicas to query, values should present in all queried replicas
- 'majority' - query all replicas, but return values present in the majority of replicas
- 'quorum' - query the majority of replicas, return values present in all of them
- 'all' - query all replicas, and return values present in all replicas
Returns:
List of points
"""
```
I have not see simarly method in FAISS implementation, how to retrieve vectors `with_vectors=True` in FAISS vectorstore?
### Motivation
I am using FAISS vectorstore, and now:
1. use FAISS added some doc and returned doc ids;
2. I want a method like in Qdrant, to retrieve the embedding from this doc ids;
Here is the Qdrant simarliy code:
```
ids_inserted = qdrant_client.add_texts(
[description],
[{
"source": "todo"
}],
)
# now I want to get the embeddings
records_inserted = qdrant_client.retrieve(
collection_name="test",
ids=ids_inserted,
with_vectors=True
)
self.embedding = records_inserted[0].vector
```
### Your contribution
Sorry, no.
|
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PR_kwDOIPDwls5XY2CP
| 8,895 |
Add base storage interface, 2 implementations and utility encoder
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[] | null | 1 | 2023-08-08T02:20:05 | 2023-08-08T21:29:07 | 2023-08-08T21:29:06 |
COLLABORATOR
| null |
This PR defines an abstract interface for key value stores.
It provides 2 implementations:
1. Local File System
2. In memory -- used to facilitate testing
It also provides an encoder utility to help take care of serialization from arbitrary data to data that can be stored by the given store
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| 8,894 |
async output parser
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PR_kwDOIPDwls5XYrZg
| 8,893 |
`PubMed` document loader
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[] | null | 1 | 2023-08-08T00:59:30 | 2023-08-08T18:38:15 | 2023-08-08T18:26:03 |
COLLABORATOR
| null |
- added `PubMed Document Loader` artifacts; ut-s; examples
- fixed `PubMed utility`; ut-s
@hwchase17
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| 1,840,386,559 |
PR_kwDOIPDwls5XYoZf
| 8,892 |
Added consistent timeout for Vectara calls
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[] | null | 1 | 2023-08-08T00:43:46 | 2023-08-08T18:10:33 | 2023-08-08T18:10:33 |
CONTRIBUTOR
| null |
- Description: consistent timeout at 60s for all calls to Vectara API
- Tag maintainer: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5XYm5W
| 8,891 |
Add OpenAI Functions request to ConsoleCallbackHandler
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[] | null | 3 | 2023-08-08T00:33:10 | 2023-09-20T17:45:22 | null |
CONTRIBUTOR
| null |
- Description: ConsoleCallbackHandler, the callback used by langchain.debug, logs responses by OpenAI function calling but does not log the `functions` parameter in the request which makes debugging difficult. This PR adds a `functions` key with the functions in the request along side the `prompts` key.
- Issue: -
- Dependencies: -
- Tag maintainer: @agola11
- Twitter handle: @finnless
The current debug output:
```
[chain/start] [1:chain:AgentExecutor] Entering Chain run with input:
{
"input": "1 + 1\n"
}
[llm/start] [1:chain:AgentExecutor > 2:llm:AzureChatOpenAI] Entering LLM run with input:
{
"prompts": [
"System: You are very powerful assistant, but bad at calculating lengths of words.\nHuman: 1 + 1"
]
}
[llm/end] [1:chain:AgentExecutor > 2:llm:AzureChatOpenAI] [1.07s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "",
"generation_info": {
"finish_reason": "function_call"
},
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "",
"additional_kwargs": {
"function_call": {
"name": "Calculator",
"arguments": "{\n \"__arg1\": \"1 + 1\"\n}"
}
}
}
}
}
]
],
"llm_output": {
"token_usage": {
"completion_tokens": 20,
"prompt_tokens": 70,
"total_tokens": 90
},
"model_name": "gpt-3.5-turbo"
},
"run": null
}
```
This PR's debug output with the new "functions" key:
```
[chain/start] [1:chain:AgentExecutor] Entering Chain run with input:
{
"input": "1 + 1"
}
[llm/start] [1:chain:AgentExecutor > 2:llm:AzureChatOpenAI] Entering LLM run with input:
{
"prompts": [
"System: You are very powerful assistant, but bad at calculating lengths of words.\nHuman: 1 + 1"
],
"functions": [
{
"name": "Calculator",
"description": "Useful for when you need to answer questions about math.",
"parameters": {
"properties": {
"__arg1": {
"title": "__arg1",
"type": "string"
}
},
"required": [
"__arg1"
],
"type": "object"
}
}
]
}
[llm/end] [1:chain:AgentExecutor > 2:llm:AzureChatOpenAI] [1.05s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "",
"generation_info": {
"finish_reason": "function_call"
},
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "",
"additional_kwargs": {
"function_call": {
"name": "Calculator",
"arguments": "{\n \"__arg1\": \"1 + 1\"\n}"
}
}
}
}
}
]
],
"llm_output": {
"token_usage": {
"completion_tokens": 20,
"prompt_tokens": 69,
"total_tokens": 89
},
"model_name": "gpt-3.5-turbo"
},
"run": null
}
```
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PR_kwDOIPDwls5XYXrU
| 8,890 |
Change default Metaphor search example to use prompt optimizer
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[] | null | 1 | 2023-08-07T22:53:18 | 2023-08-08T00:27:00 | 2023-08-08T00:25:37 |
CONTRIBUTOR
| null |
- fix install command
- change example notebook to use Metaphor autoprompt by default
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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2. an example notebook showing its use.
Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5XYTdV
| 8,889 |
rfc: make lc pydantic version agnostic
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[] | null | 4 | 2023-08-07T22:29:23 | 2023-08-11T18:34:44 | 2023-08-11T18:34:44 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5XYShM
| 8,888 |
Fix metaphor install command in guide
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[] | null | 2 | 2023-08-07T22:24:23 | 2023-08-07T22:43:48 | 2023-08-07T22:43:48 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5tr8yC
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chunk_size arg is not used in OpenAIEmbeddings's embed_documents and aembed_documents methods
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[] | null | 2 | 2023-08-07T21:34:25 | 2023-11-14T16:06:01 | 2023-11-14T16:06:00 |
NONE
| null |
### System Info
v0.0.256
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Add `print(f"chunk-size: {_chunk_size}")` in OpenAIEmbeddings._get_len_safe_embeddings after `_chunk_size = chunk_size or self.chunk_size`, and pass chunk_size argument to embed_documents other than the default value of 1000. 1000 will be printed instead of the chunk_size passed as argument.
### Expected behavior
chunk_size arg is not used in OpenAIEmbeddings's embed_documents and aembed_documents methods. It should be passed to self._get_len_safe_embeddings.
Check: https://github.com/langchain-ai/langchain/blob/v0.0.256/libs/langchain/langchain/embeddings/openai.py#L463
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PR_kwDOIPDwls5XYEaD
| 8,886 |
update metaphor docs
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[] | null | 1 | 2023-08-07T21:18:23 | 2023-08-07T21:44:42 | 2023-08-07T21:44:41 |
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Bagatur/docsly
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[] | null | 1 | 2023-08-07T20:58:38 | 2023-08-11T21:34:30 | 2023-08-11T21:34:30 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5try3k
| 8,884 |
Simplify linting workflow by adding to pre-commit
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[] | null | 3 | 2023-08-07T20:57:44 | 2023-11-16T16:06:26 | 2023-11-16T16:06:25 |
CONTRIBUTOR
| null |
### Feature request
## Description
Add format/lint actions to the pre-commit tool, so user's don't have to run these locally on their machines. This is a 2 stage process:
1. Make this part of the local commit process. This will ensure that all commits run these explicitly before pushing the change.
2. Adding this to the CI will ensure that users who choose to ignore setting up the pre-commit in their local repo, can rely on the CI to fix some of the formatting and linting (fixable). This also avoids some of the system specific issues/differences that users might end up having on their local machines (stale versions of lint tools) by ensuring that the CI is running these on one consistent up-to-date platform.
### Motivation
The current manual linting process adds churn and friction to the dev workflow as most devs end up pushing their changes without running these steps, and rely on the CI to remind them on running them. Some of the formatting and linting can be automated with CI, and does not require a dev's direct involvement.
### Your contribution
I am happy to work on this, but would need the core team's input before going ahead with this change.
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PR_kwDOIPDwls5XX-I0
| 8,883 |
Fix handling of ServerDisconnectedError in AsyncHtmlLoader that crash…
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NONE
| null |
I've updated the WebResearchRetriever to address a bug where it would crash if unable to retrieve content from a URL. I've implemented error handling and added checks to prevent this issue.
@rlancemartin, @eyurtsev, @agola11
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PR_kwDOIPDwls5XX8NB
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Fixes typo in documentation
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[] | null | 2 | 2023-08-07T20:46:22 | 2023-08-07T21:33:22 | 2023-08-07T21:33:22 |
CONTRIBUTOR
| null |
Fixes a simple typo in the google search engine tool documentation
@baskaryan
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| 1,840,131,332 |
I_kwDOIPDwls5tri0E
| 8,881 |
conversational-retrieval-agent using AgentTokenBufferMemory : Still cannot prevent maximum content length errors
|
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[] | null | 3 | 2023-08-07T20:04:21 | 2023-08-11T12:21:33 | 2023-08-11T12:21:00 |
CONTRIBUTOR
| null |
### System Info
Based on https://github.com/hwchase17/conversational-retrieval-agent/blob/master/streamlit.py, did few changes to code.
Since the beginning of the tests could not prevent the max content length error.
the best change was using:
memory = AgentTokenBufferMemory(memory_key="history", llm=llm, max_history=5, max_token_limit= 3000)
llm = ChatOpenAI(temperature=0, streaming=True, model="gpt-3.5-turbo",max_tokens=500)
It took longer but still got the error message. All the conversation was logged using LangSmith , username [email protected] ,, https://smith.langchain.com/projects/p/2e2c3411-5910-4785-985d-a4b31702e6c7/r/2893c334-9650-4125-99df-af6e18481b12
It seemed that the context was under control, but the latest question broke it.
Python version - 3.11.3
Langchain - 0.0.252
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
memory = AgentTokenBufferMemory(memory_key="history", llm=llm, max_history=5, max_token_limit= 3000)
llm = ChatOpenAI(temperature=0, streaming=True, model="gpt-3.5-turbo",max_tokens=500)
### Expected behavior
All the conversation was logged using LangSmith , username [email protected] ,, https://smith.langchain.com/projects/p/2e2c3411-5910-4785-985d-a4b31702e6c7/r/2893c334-9650-4125-99df-af6e18481b12
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PR_kwDOIPDwls5XXs9K
| 8,880 |
Add LabelStudio integration
|
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[] | null | 5 | 2023-08-07T19:51:53 | 2023-08-11T18:24:10 | 2023-08-11T18:24:10 |
CONTRIBUTOR
| null |
This PR introduces [Label Studio](https://labelstud.io/) integration with LangChain via `LabelStudioCallbackHandler`:
- sending data to the Label Studio instance
- labeling dataset for supervised LLM finetuning
- rating model responses
- tracking and displaying chat history
- support for custom data labeling workflow
### Example
```
chat_llm = ChatOpenAI(callbacks=[LabelStudioCallbackHandler(mode="chat")])
chat_llm([
SystemMessage(content="Always use emojis in your responses."),
HumanMessage(content="Hey AI, how's your day going?"),
AIMessage(content="🤖 I don't have feelings, but I'm running smoothly! How can I help you today?"),
HumanMessage(content="I'm feeling a bit down. Any advice?"),
AIMessage(content="🤗 I'm sorry to hear that. Remember, it's okay to seek help or talk to someone if you need to. 💬"),
HumanMessage(content="Can you tell me a joke to lighten the mood?"),
AIMessage(content="Of course! 🎭 Why did the scarecrow win an award? Because he was outstanding in his field! 🌾"),
HumanMessage(content="Haha, that was a good one! Thanks for cheering me up."),
AIMessage(content="Always here to help! 😊 If you need anything else, just let me know."),
HumanMessage(content="Will do! By the way, can you recommend a good movie?"),
])
```
<img width="906" alt="image" src="https://github.com/langchain-ai/langchain/assets/6087484/0a1cf559-0bd3-4250-ad96-6e71dbb1d2f3">
### Dependencies
- [label-studio](https://pypi.org/project/label-studio/)
- [label-studio-sdk](https://pypi.org/project/label-studio-sdk/)
https://twitter.com/labelstudiohq
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Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
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| 1,840,099,298 |
PR_kwDOIPDwls5XXpjK
| 8,879 |
scheduled tests GHA
|
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[] | null | 1 | 2023-08-07T19:38:41 | 2023-08-08T21:55:26 | 2023-08-08T21:55:25 |
COLLABORATOR
| null |
Adding scheduled daily GHA that runs marked integration tests. To start just marking some tests in test_openai
|
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PR_kwDOIPDwls5XXTX_
| 8,878 |
Fix colab link for extraction ntbk
|
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[] | null | 1 | 2023-08-07T18:21:12 | 2023-08-07T18:36:47 | 2023-08-07T18:36:46 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
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I_kwDOIPDwls5tq9DE
| 8,877 |
Unexpected Azure Dependency with OpenAI and Langchain Library in Next.js App Deployed on AWS Amplify
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[] | null | 5 | 2023-08-07T18:07:37 | 2023-09-01T10:55:18 | 2023-09-01T10:55:18 |
NONE
| null |
### System Info
langchain 0.0.256
Python 3.8
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I've developed a Next.js application that uses the Langchain library for chat functionality and is deployed on AWS Amplify. The application works perfectly when running locally, but fails after deployment on AWS Amplify.
The application uses the Langchain library, OpenAIEmbeddings for generating embeddings, and PineconeStore for storing vectors. I've ensured that my environment variables are correctly set up both locally and in the AWS Amplify console.
Here is the code snippet for my chat handler:
```
import type { NextApiRequest, NextApiResponse } from 'next';
import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
import { PineconeStore } from 'langchain/vectorstores/pinecone';
import { makeChain } from '@/utils/makechain';
import { pinecone } from '@/utils/pinecone-client';
import { PINECONE_INDEX_NAME, PINECONE_NAME_SPACE } from '@/config/pinecone';
export default async function handler(
req: NextApiRequest,
res: NextApiResponse,
) {
const { question, history } = req.body;
if (req.method !== 'POST') {
res.status(405).json({ error: 'Method not allowed' });
return;
}
if (!question) {
return res.status(400).json({ message: 'No question in the request' });
}
const sanitizedQuestion = question.trim().replaceAll('\n', ' ');
try {
const index = pinecone.Index(PINECONE_INDEX_NAME);
const vectorStore = await PineconeStore.fromExistingIndex(
new OpenAIEmbeddings(),
{
pineconeIndex: index,
textKey: 'text',
namespace: PINECONE_NAME_SPACE,
},
);
const chain = makeChain(vectorStore);
const response = await chain.call({
question: sanitizedQuestion,
chat_history: history || [],
});
res.status(200).json(response);
} catch (error: any) {
console.log('chat.ts file error: ', error);
res.status(500).json({ error: error.message || 'Something went wrong' });
}
}
```
### Expected behavior
The error message I'm receiving suggests that the Langchain library is assuming I am running on an Azure instance and is expecting an Azure-specific environment variable. However, I am not using Azure, I'm using AWS. The error message states that an 'azureOpenAIApiInstanceName' is missing, which, as I understand, is only relevant if I was using Azure.
Has anyone encountered a similar issue, or have any insights into why this might be happening? I've been unable to find any information in the Langchain library documentation about this Azure dependency.
|
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| 1,839,971,483 |
PR_kwDOIPDwls5XXNqE
| 8,876 |
Introduce Nebula LLM to LangChain
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[] | null | 3 | 2023-08-07T18:03:22 | 2023-08-07T23:49:53 | 2023-08-07T20:15:26 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
## Description
This PR adds Nebula to the available LLMs in LangChain.
Nebula is an LLM focused on conversation understanding and enables users to extract conversation insights from video, audio, text, and chat-based conversations. These conversations can occur between any mix of human or AI participants.
Examples of some questions you could ask Nebula from a given conversation are:
- What could be the customer’s pain points based on the conversation?
- What sales opportunities can be identified from this conversation?
- What best practices can be derived from this conversation for future customer interactions?
You can read more about Nebula here:
https://symbl.ai/blog/extract-insights-symbl-ai-generative-ai-recall-ai-meetings/
#### Integration Test
An integration test is added, but it requires network access. Since Nebula is fully managed like OpenAI, network access is required to exercise the integration test.
#### Linting
- [x] make lint
- [x] make test (TODO: there seems to be a failure in another non-related test??? Need to check on this.)
- [x] make format
### Dependencies
No new dependencies were introduced.
### Twitter handle
[@symbldotai](https://twitter.com/symbldotai)
[@dvonthenen](https://twitter.com/dvonthenen)
If you have any questions, please let me know.
cc: @hwchase17, @baskaryan
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I_kwDOIPDwls5tq5_L
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Issue: ConversationalRetrievalChain.from_llm sometime answer with the following up question
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[] | null | 4 | 2023-08-07T17:57:21 | 2023-11-16T16:06:26 | 2023-11-16T16:06:25 |
NONE
| null |
### Issue you'd like to raise.
Hi, Im using ConversationalRetrievalChain.from_llm and I notice that sometimes the answer is the following up question.
Also sometimes the answer generates a few Q and A all togheter.
Anyone with the same problem?
regards.
### Suggestion:
_No response_
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I_kwDOIPDwls5tqv0_
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ChatOpenAI agenerate does not use internal _agenerate and does not support message roles
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NONE
| null |
### System Info
langchain==0.0.250
Python 3.11.3
22.5.0 Darwin Kernel Version 22.5.0
### Who can help?
@agola11
### 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
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
This code runs perfectly well if `agenerate` is replaced with `_agenerate`, meaning that the super function is not using the internal implementation correctly.
```
llm = ChatOpenAI(temperature=0.7, max_tokens=max_tokens)
messages = [
SystemMessagePromptTemplate.from_template("You are a funny chatbot"),
HumanMessagePromptTemplate.from_template("Tell me a joke about {topic}")
]
ChatPromptTemplate.from_messages(
messages
)
formatted_messages = chat_prompt.format_prompt(
topic='horses'
).to_messages()
async def async_generate(llm, formatted_messages):
return await llm.agenerate(messages=formatted_messages)
res = await async_generate(llm, formatted_messages)
```
### Expected behavior
agenerate should work the same as _agenerate
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Create ArcGISLoader & example notebook
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CONTRIBUTOR
| null |
- Description: Adds the ArcGISLoader class to `langchain.document_loaders`
- Allows users to load data from ArcGIS Online, Portal, and similar
- Users can authenticate with `arcgis.gis.GIS` or retrieve public data anonymously
- Uses the `arcgis.features.FeatureLayer` class to retrieve the data
- Defines the most relevant keywords arguments and accepts `**kwargs`
- Dependencies: Using this class requires `arcgis` and, optionally, `bs4.BeautifulSoup`.
Tagging maintainers:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5XWd2l
| 8,872 |
fix: Resolve `AttributeError` in Google Cloud Enterprise Search retriever
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[] | null | 4 | 2023-08-07T15:42:52 | 2023-08-08T19:11:13 | 2023-08-08T19:11:13 |
CONTRIBUTOR
| null |
- Reverting some of the changes made in https://github.com/langchain-ai/langchain/pull/8369
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I_kwDOIPDwls5tqEdY
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Quality of answers gets drastically bad with longer chains in Langchain
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[] | null | 5 | 2023-08-07T15:40:13 | 2023-11-19T16:05:51 | 2023-11-19T16:05:50 |
NONE
| null |
I think most of the people who works with Langchain in building products or automating stuff noticed how bad the quality of answers degrades when you ask multiple of questions using the same chain (e.g: the chain gets longer). It gets to answer the first 2 or 3 questions right then it may hallucinate returning a wrong answer, it may fail to answer your question or even crash regardless of the GPT model used.
Is there a way anyone found in order to optimize this? One way I always try is to prompt engineering things but it doesn't help as much.
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PR_kwDOIPDwls5XWUMH
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bump 256
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| null | null |
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PR_kwDOIPDwls5XWGUB
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Add user_context to AmazonKendraRetriever
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[] | null | 3 | 2023-08-07T14:54:20 | 2023-08-08T15:37:04 | 2023-08-08T15:37:04 |
CONTRIBUTOR
| null |
### Description
Now, we can pass information like a JWT token using user_context:
```python
self.retriever = AmazonKendraRetriever(index_id=kendraIndexId, user_context={"Token": jwt_token})
```
- [x] `make lint`
- [x] `make format`
- [x] `make test`
Also tested by pip installing in my own project, and it allows access through the token.
### Maintainers
@rlancemartin, @eyurtsev
### My twitter handle
[girlknowstech](https://twitter.com/girlknowstech)
### Tests passed
```
-------------------------------------------------------------------------------------------------------- snapshot report summary --------------------------------------------------------------------------------------------------------
22 snapshots passed.
========================================================================================================== slowest 5 durations ==========================================================================================================
4.01s call tests/unit_tests/llms/test_openai.py::test_openai_async_retries
4.01s call tests/unit_tests/llms/test_openai.py::test_openai_retries
0.76s call tests/unit_tests/memory/chat_message_histories/test_sql.py::test_multiple_sessions[SQLite]
0.67s call tests/unit_tests/document_loaders/parsers/test_pdf_parsers.py::test_pdfminer_parser
0.65s call tests/unit_tests/memory/chat_message_histories/test_sql.py::test_clear_messages[SQLite]
============================================================================================ 1022 passed, 83 skipped, 47 warnings in 21.99s =============================================================================================
```
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description:
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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Maintainer responsibilities:
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PR_kwDOIPDwls5XVgbd
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Allow to specify a custom loader for GcsFileLoader
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CONTRIBUTOR
| null |
#8863
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| 1,839,461,670 |
I_kwDOIPDwls5to_Um
| 8,867 |
Language support for Agents
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[] | null | 6 | 2023-08-07T13:28:41 | 2023-10-24T22:23:21 | null |
CONTRIBUTOR
| null |
### Feature request
Agents can deal with prompts in different languages, but they might get confused or more unreliable if the internal prompts (e.g. format structure with the action, action input, thought etc.) are still in English.
That's why I thought about an additional language option when initializing an agent. I tried to come up with an idea of how to implement this efficiently, but I found it particularly hard to implement it, so it can be used for every language.
My initial thought was to specify the necessary prompts in a different language and initialize the agent with an extra keyword. With this approach only a finite number of languages can be covered and it could end up in a mess to save all the prompts in different languages.
My second thought was to initialize the agent with a language keyword and then translate all the prompts in the desired language. That could be too much of a hustle for just initializing an agent with prompts that are actually known.
I wanted to share this and see if this is actually something that could be useful, and maybe gather some ideas of how to implement it.
### Motivation
I've implemented an MRKL agent that writes a detailed article on a specified topic based on internet search. In my specific case, it should write an article about topics related to Germany, e.g. the drought situation in Germany. Naively, my first attempt was to give it a prompt like _Write a detailed article about the drought situation in Germany. The article must be written in German language_.
I discovered two problems:
1. As I was giving the prompt in English, the model "thought" in English as well. Therefore, the action inputs were in English, too. This led to the result that all search results were also in English and primarily from sources (e.g. The Guardian, BBC) outside of Germany reporting about the drought situation in Germany. What I would have liked instead are search results from German newspapers or similar.
2. The text generation was very much inconsistent. It wasn't quite able to write the final article in German, as all information was given in English. Sometimes, it managed to write the text in German, but I also wrote all intermediate steps (action, action input, Observation, Thought, etc.) in German, too. That led to the problem of not finding the tool (tool descriptions were also in English) and resulting OutputParser Error, because the English keywords _thought_ and _action_ etc. could be found.
So my goal was to create an agent that solely worked in the German language. Meaning, that the prompt, intermediate steps, search results and the final output are all in German.
So what I did was reimplementing all the necessary MRKL agent functions and templates in a German version. It worked out and the results were as expected and reliable.
I think that agents with different language support could be beneficial for more users. You can give it a prompt in other languages but that the agent's internal prompts are in specific language, too.
### Your contribution
I am not really sure how to do that in a good way, so that multiple languages are supported for all the agents, and if that's even desired. But if there is a good strategy, I would like to try to implement it.
|
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Issue: Supabase python client pydantic dependency mismatch
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NONE
| null |
### Issue you'd like to raise.
When using supabase client I get dependency errors, which I believe originate from the pydantic library
Supabase uses requires pydantic<3.0,>=2.1.0
Langchain requires (I guess) 1.10.12
With langchain compatible pydantic versions, supabase errors out.
``` python
from supabase.client import Client, create_client
supabase_url = os.environ.get("SUPABASE_URL")
supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
supabase: Client = create_client(supabase_url, supabase_key)
```
ImportError: cannot import name 'field_validator' from 'pydantic'
But if I upgrade pydantic, langchain starts erroring out.
``` python
from langchain.vectorstores.pgvector import PGVector
```
PydanticUserError: If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`. Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.
Is there any quick fix for this?
Thanks
|
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PR_kwDOIPDwls5XVM3-
| 8,865 |
bump 255
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[] | null | 1 | 2023-08-07T12:42:56 | 2023-08-07T14:25:24 | 2023-08-07T14:25:23 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5togrE
| 8,864 |
Issue: How can we combine ConversationalRetrievalChain with openai ChatCompletion
|
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[] | null | 2 | 2023-08-07T12:19:11 | 2023-08-08T19:26:55 | 2023-08-08T19:25:47 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I'm trying to chat with a PDF using ConversationalRetrievalChain.
```
embeddings = OpenAIEmbeddings()
vectordb = Chroma(embedding_function=embeddings, persist_directory=directory)
qa_chain = ConversationalRetrievalChain.from_llm(ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.3), vectordb.as_retriever(), memory=memory)
answer = (qa_chain({"question": query}))
```
It works perfectly as it gives the answer from the documents. But it can't alter the tone or even convert the answer into another language when prompted.
How can we change the tone like we do in openai ChatCompletion:
```
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful and friendly chatbot who converts text to a very friendly tone."},
{"role": "user", "content": f"{final_answer}"}
]
)
```
such that it answers from the doc but also converts it according to some given prompt. Right now I have to pass the received output from ConversationalRetrievalChain in above code in order to modify the tone. Is this kind of functionality missing in ConversationalRetrievalChain?
### Suggestion:
_No response_
|
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Allow GCSFileLoader to use alternative loaders for individual files
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[] | null | 1 | 2023-08-07T11:56:01 | 2023-08-08T12:14:10 | 2023-08-08T12:14:10 |
CONTRIBUTOR
| null |
### Feature request
Currently GCSFileLoader uses UnstructuredFileLoader in a pre-defined mode, but it would be nice to allow to specify different pdf or other loaders.
### Motivation
Allow to specify loaders when working with files from GCS.
### Your contribution
yes, I'm happy to.
|
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| 1,839,272,930 |
PR_kwDOIPDwls5XU2Fv
| 8,862 |
Add flush=True to stream examples
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[] | null | 1 | 2023-08-07T11:38:27 | 2023-08-07T18:33:19 | 2023-08-07T18:33:18 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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I_kwDOIPDwls5toJkv
| 8,860 |
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[] | null | 3 | 2023-08-07T11:20:43 | 2023-11-13T16:06:01 | 2023-11-13T16:06:00 |
NONE
| null |
### Feature request
Can we get ttl and sessionId in UpstashRedisCache ?
### Motivation
cleaning cache
### Your contribution
suggestion
|
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PR_kwDOIPDwls5XUk9H
| 8,859 |
fix evaluation parse test
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[] | null | 1 | 2023-08-07T10:51:11 | 2023-08-07T18:15:41 | 2023-08-07T18:15:41 |
CONTRIBUTOR
| null |
# What
- fix evaluation parse test
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Fix evaluation parse test
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MLOpsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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I_kwDOIPDwls5tnwmE
| 8,858 |
TypeError("'NoneType' object is not callable")
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[] | null | 4 | 2023-08-07T10:15:56 | 2023-11-16T16:06:31 | 2023-11-16T16:06:30 |
NONE
| null |
### System Info
OS: Redhat 8
Python: 3.9
Langchain: 0.0.246
### 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
So i initially reported this bug with mlflow, bug upon further investigation it is related to langchain.
Following code is simple representation of bigger code. I will also post that in bottom.
===================================================================
Code:
<pre><code>
import os
from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFacePipeline
os.environ["CURL_CA_BUNDLE"] = ""
if True: # run the following code to download the model flan-t5-small from huggingface.co
from transformers import pipeline
model = pipeline(model="google/flan-t5-small") #'text2text-generation'
model.save_pretrained("/tmp/model/flan-t5-small")
llm = HuggingFacePipeline.from_model_id(
model_id="/tmp/model/flan-t5-small",
task="text2text-generation",
model_kwargs={"temperature": 1e-10},
)
template = """Translate everything you see after this into French:
{input}
"""
prompt = PromptTemplate(template=template, input_variables=["input"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain("my name is John")) # works
llm_chain.save("llm_chain.json")
from langchain.chains import load_chain
m = load_chain("llm_chain.json")
print(m("my name is John"))
</code></pre>
Error trace:
<code><pre>
{'input': 'my name is John', 'text': " toutefois, je suis en uvre à l'heure"}
Traceback (most recent call last):
File "a.py", line 37, in <module>
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/base.py", line 258, in __call__
raise e
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/base.py", line 252, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/llm.py", line 92, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/llm.py", line 102, in generate
return self.llm.generate_prompt(
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 451, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 582, in generate
output = self._generate_helper(
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 488, in _generate_helper
raise e
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 475, in _generate_helper
self._generate(
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 961, in _generate
self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/huggingface_pipeline.py", line 168, in _call
response = self.pipeline(prompt)
TypeError: 'NoneType' object is not callable
</code></pre>
===================================================================
Original Code where this bug occurred (MLFlow):
<code></pre>
import mlflow
from datetime import datetime
import logging
logging.getLogger("mlflow").setLevel(logging.DEBUG)
from langchain import PromptTemplate, LLMChain, HuggingFaceHub
from langchain.llms import HuggingFacePipeline
import os
def now_str():
return datetime.now().strftime("%Y%m%d%H%M%S")
os.environ["CURL_CA_BUNDLE"] = ""
if True:
from transformers import pipeline
model = pipeline(model="google/flan-t5-small") #'text2text-generation'
model.save_pretrained("/tmp/model/flan-t5-small")
llm = HuggingFacePipeline.from_model_id(
model_id="/tmp/model/flan-t5-small",
task="text2text-generation",
model_kwargs={"temperature": 1e-10},
)
template = """Translate everything you see after this into French:
{input}
"""
prompt = PromptTemplate(template=template, input_variables=["input"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain.run("my name is John")) # This is working !!
### Output: {'input': 'my name is John', 'text': "j'ai le nom de John"}
experiment_id = mlflow.create_experiment(f"HF_LLM_{now_str()}")
with mlflow.start_run(experiment_id=experiment_id) as run:
logged_model = mlflow.langchain.log_model(
lc_model=llm_chain,
artifact_path="HF_LLM",
)
m = mlflow.langchain.load_model(logged_model.model_uri)
m.run("my name is John")
</code></pre>
Error Trace:
<code><pre>
Traceback (most recent call last):
File "a.py", line 51, in <module>
m.run("my name is John")
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/base.py", line 451, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/base.py", line 258, in __call__
raise e
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/base.py", line 252, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/llm.py", line 92, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/chains/llm.py", line 102, in generate
return self.llm.generate_prompt(
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 451, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 582, in generate
output = self._generate_helper(
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 488, in _generate_helper
raise e
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 475, in _generate_helper
self._generate(
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/base.py", line 961, in _generate
self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
File "/home/haru/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/langchain/llms/huggingface_pipeline.py", line 168, in _call
response = self.pipeline(prompt)
TypeError: 'NoneType' object is not callable
</code></pre>
===================================================================
Another Example:
<code><pre>
from datetime import datetime
import logging
logging.getLogger("mlflow").setLevel(logging.DEBUG)
from langchain import PromptTemplate, LLMChain, HuggingFaceHub
from langchain.llms import HuggingFacePipeline
import os
def now_str():
return datetime.now().strftime("%Y%m%d%H%M%S")
os.environ["CURL_CA_BUNDLE"] = ''
if (True): # run the following code to download the model flan-t5-large from huggingface.co
from transformers import pipeline
model= pipeline(model="google/flan-t5-large") #'text2text-generation'
model.save_pretrained("/tmp/model/flan-t5-large")
llm = HuggingFacePipeline.from_model_id(model_id="/tmp/model/flan-t5-large", task="text2text-generation", model_kwargs={"temperature":1e-10})
template = """Translate everything you see after this into French:
{input}
"""
prompt = PromptTemplate(template=template, input_variables=["input"])
llm_chain = LLMChain(
prompt=prompt,
llm=llm
)
llm_chain("my name is John") # This is working !!
#Output: {'input': 'my name is John', 'text': "j'ai le nom de John"}
experiment_id = mlflow.create_experiment(f'HF_LLM_{now_str()}')
with mlflow.start_run(experiment_id=experiment_id) as run:
logged_model = mlflow.langchain.log_model(
lc_model=llm_chain,
artifact_path="HF_LLM",
)
=============== This throws error ==============
input_str = "My name is John"
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
output = loaded_model.predict(
[
{
"input": input_str
},
{
"input": "Do you like coffee?"
}
]
)
print(output)
</code></pre>
Error Trace:
<code><pre>
2023/08/07 08:57:08 WARNING mlflow.langchain.api_request_parallel_processor: Request #0 failed with TypeError("'NoneType' object is not callable")
2023/08/07 08:57:08 WARNING mlflow.langchain.api_request_parallel_processor: Request #1 failed with TypeError("'NoneType' object is not callable")
---------------------------------------------------------------------------
MlflowException Traceback (most recent call last)
<ipython-input-19-29b0feddacd1> in <cell line: 3>()
3 with project.setup_mlflow(mf) as mlflow:
4 loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
----> 5 output = loaded_model.predict(
6 [
7 {
/hadoopfs/fs1/dataiku/data_dir/code-envs/python/mlflow_25_python_39/lib/python3.9/site-packages/mlflow/pyfunc/__init__.py in predict(self, data)
426 raise
427
--> 428 return self._predict_fn(data)
429
430 @experimental
/hadoopfs/fs1/dataiku/data_dir/code-envs/python/mlflow_25_python_39/lib/python3.9/site-packages/mlflow/langchain/__init__.py in predict(self, data)
654 "Input must be a pandas DataFrame or a list of strings or a list of dictionaries",
655 )
--> 656 return process_api_requests(lc_model=self.lc_model, requests=messages)
657
658
/hadoopfs/fs1/dataiku/data_dir/code-envs/python/mlflow_25_python_39/lib/python3.9/site-packages/mlflow/langchain/api_request_parallel_processor.py in process_api_requests(lc_model, requests, max_workers)
138 # after finishing, log final status
139 if status_tracker.num_tasks_failed > 0:
--> 140 raise mlflow.MlflowException(
141 f"{status_tracker.num_tasks_failed} tasks failed. See logs for details."
142 )
MlflowException: 2 tasks failed. See logs for details.
</code></pre>
Looks like langchain doesn't restore the pipeline.
### Expected behavior
Langhain loads the chain successfully.
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| 1,838,913,498 |
I_kwDOIPDwls5tm5fa
| 8,857 |
Bug with os.environ['FAISS_NO_AVX2'] = '1'
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open
| false | null |
[] | null | 3 | 2023-08-07T08:04:40 | 2023-11-12T07:47:12 | null |
NONE
| null |
### System Info
I was trying to create FAISS embeddings that would work on different platforms so I tried to use:
`os.environ['FAISS_NO_AVX2'] = '1' `
as recommended in https://github.com/langchain-ai/langchain/blob/6cdd4b5edca511b0015f1b39102225fe638d8359/langchain/vectorstores/faiss.py
It works for windows, but I am getting `TypeError: IndexFlatCodes.add() missing 1 required positional argument: 'x'` when I try to create embeddings in Docker image
Full error:
```
TypeError: IndexFlatCodes.add() missing 1 required positional argument: 'x'
Traceback:
File "/usr/local/lib/python3.11/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 565, in _run_script
exec(code, module.__dict__)
File "/app/src/pages/1_💬__AI-Chat.py", line 127, in <module>
chatbot = utils.setup_chatbot(
^^^^^^^^^^^^^^^^^^^^
File "/app/./src/modules/utils.py", line 121, in setup_chatbot
vectors = embeds.getDocEmbeds(file, uploaded_file.name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/./src/modules/embedder.py", line 104, in getDocEmbeds
self.storeDocEmbeds(file, original_filename)
File "/app/./src/modules/embedder.py", line 86, in storeDocEmbeds
vectors = FAISS.from_documents(data, embeddings)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain/vectorstores/base.py", line 336, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain/vectorstores/faiss.py", line 550, in from_texts
return cls.__from(
^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain/vectorstores/faiss.py", line 505, in __from
index.add(vector)
```
langchain==0.0.226
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8",csv_args={
'delimiter': ',',})
data = loader.load()
embeddings = OpenAIEmbeddings(...)
vectors = FAISS.from_documents(data, embeddings)
### Expected behavior
embeddings should generated
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PR_kwDOIPDwls5XTjhI
| 8,856 |
Fix/abstract add message
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[] | null | 1 | 2023-08-07T07:53:03 | 2023-08-07T18:02:20 | 2023-08-07T18:02:20 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Fix/abstract add message
- Issue: None
- Dependencies: None
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @MLOpsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,838,870,679 |
I_kwDOIPDwls5tmvCX
| 8,855 |
Separate LLMs for Tool Selection and Chatting in Agent Initialization
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[] | null | 1 | 2023-08-07T07:38:07 | 2023-11-13T16:06:11 | 2023-11-13T16:06:10 |
NONE
| null |
### Feature request
Currently when we initialize an agent we can only pass one llm which is responsible for both tool selection and chatting. There should be a feature that would allows us to define seperate llms for tool selection and chatting respectively.
### Motivation
It has been observed that text llms are better at decision making which is helpful when we have multiple tools but their chat responses are not that impressive. On the other hand chat models are better at chatting and but not as good at decision making which causes problems when we have multiple tools. Therefore, I want to harness the good of both in single agent.
### Your contribution
Yes I can submit a PR if certain guidance is provided.
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I_kwDOIPDwls5tmkOc
| 8,854 |
Issue: Having trouble passing question with LangSmith
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[] | null | 1 | 2023-08-07T07:08:48 | 2023-08-08T03:07:45 | 2023-08-08T03:07:44 |
NONE
| null |
### Issue you'd like to raise.
Hello! I am trying to use LangSmith to test my LLM. I've created a dataset, but I'm confused, as this dataset doesn't seem to work even though I am using `StuffDocumentsChain` in testing (I think) the same way I'm using it in production. Here's how I call it in production:
```
docs_chain = StuffDocumentsChain(
llm_chain=LLMChain(
llm=ChatOpenAI(
temperature=0,
),
prompt=prompt,
verbose=True,
),
document_variable_name="context",
verbose=True,
document_prompt=document_prompt,
)
...
answer = await docs_chain.arun(
input_documents=llm_context["docs"], **llm_context["inputs"]
)
...
```
So, this accept 4 inputs (I believe coming from `input_documents` and `llm_context["inputs"]`: `'question', 'chat_history', 'input_documents', 'organization_uuid'`
However, when I want to test this with LangSmith I'm trying to do:
```
def create_chain():
messages = [SystemMessagePromptTemplate.from_template(system_prompt)]
messages.append(HumanMessagePromptTemplate.from_template('{question}'))
retrieval_prompt = ChatPromptTemplate.from_messages(messages)
combine_docs_chain = StuffDocumentsChain(
llm_chain=LLMChain(
llm=ChatOpenAI(
temperature=0,
),
prompt=retrieval_prompt,
verbose=True,
),
document_variable_name="context",
verbose=True,
document_prompt=document_prompt,
)
return combine_docs_chain
...
await arun_on_dataset(
client=client,
dataset_name=dataset_name,
llm_or_chain_factory=create_chain,
evaluation=eval_config,
verbose=True,
)
```
but I'm getting:
```
langchain.smith.evaluation.runner_utils.InputFormatError: Example inputs do not match chain input keys. Please provide an input_mapper to convert the example.inputs to a compatible format for the chain you wish to evaluate.Expected: ['input_documents']. Got: dict_keys(['question', 'chat_history', 'input_documents', 'organization_uuid'])
```
I don't understand this, as I'm calling this in the same way... shouldn't those input map in the same way?
As the error states, I can also pass an `input_mapper` to just convert my example inputs into the required format, but then I would be throwing away the `question`, etc., and I want that `question` to be part of the test.
I'm probably thinking about this wrong. Let me know what you would do here, thank you!
### Suggestion:
_No response_
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| 1,838,824,280 |
PR_kwDOIPDwls5XTUmz
| 8,853 |
Bagel master
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[] | null | 1 | 2023-08-07T07:08:13 | 2023-08-07T07:08:43 | 2023-08-07T07:08:43 |
CONTRIBUTOR
| null | null |
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I_kwDOIPDwls5tmYT0
| 8,852 |
Issue: AssertionError: Unknown arguments: ['fetch_k', 'maximal_marginal_relevance'] when specifyig search_kwargs ini from_llm() function call
|
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[] | null | 1 | 2023-08-07T06:33:40 | 2023-08-07T16:20:46 | 2023-08-07T16:20:46 |
NONE
| null |
### Issue you'd like to raise.
Hi there, I am recently using Langchain to build my toy chatbot. I used both **Qdrant** cloud storage and local storage for test. However, when I am using local storage for **vector_store** and **search_kwargs**, everything is good. But, when I switch to the Qdrant cloud storage, the retriever of vectorDB is reporting no machtching **search_kwargs**. My partial codes shown below.
<img width="947" alt="image" src="https://github.com/langchain-ai/langchain/assets/59675745/50d0e930-38a8-4d69-b294-656435a5fdc3">
The error:
<img width="1012" alt="image" src="https://github.com/langchain-ai/langchain/assets/59675745/f800c82c-ac75-4158-8dc2-ae32a271b7c0">
### Suggestion:
_No response_
|
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| 1,838,761,710 |
I_kwDOIPDwls5tmUbu
| 8,851 |
Visualize the Networkx Entity Graph created via the GraphIndexCreator class using pyvis/matplotlib
|
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[] | null | 2 | 2023-08-07T06:20:54 | 2023-11-13T16:06:16 | 2023-11-13T16:06:15 |
NONE
| null |
### Feature request
```
from langchain.indexes import GraphIndexCreator
from langchain.graphs.networkx_graph import KnowledgeTriple
index_creator = GraphIndexCreator(llm=llm)
graph = index_creator.from_text('text to RDF')
graph.add_triple(KnowledgeTriple('aaa', '10', 'bbb'))
graph.add_triple(KnowledgeTriple('ccc', '30', 'ddd'))
graph.add_triple(KnowledgeTriple('eee', '10', 'aaa'))
graph.add_triple(KnowledgeTriple('ffff', '5', 'ddd'))
```
Using above code I have created a sample Networkx graph. Now I want to visualize this graph using pyvis/matplotlib. How can I?
### Motivation
Graph visualization support
### Your contribution
Graph visualization support
|
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| 1,838,703,116 |
I_kwDOIPDwls5tmGIM
| 8,850 |
Issue: Support for Custom VertexAI models
|
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[] | null | 1 | 2023-08-07T05:22:00 | 2023-11-13T16:06:20 | 2023-11-13T16:06:19 |
NONE
| null |
### Issue you'd like to raise.
This is more like a question than feature request.
Google Vertex AI has support for some task specific models which are specially trained for certain action.
I know Langchain has support for LLM based models in general but do you ever plan to integrate these task specific models in your library? This (by Google's definition) is a Language Service Model but might not be powered by Gen AI technology
<img width="932" alt="image" src="https://github.com/langchain-ai/langchain/assets/97156231/867a3101-e429-4716-a3ae-62c3c4c90813">
### Suggestion:
_No response_
|
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I_kwDOIPDwls5tmBLe
| 8,849 |
RequestError: RequestError(400, 'search_phase_execution_exception...) with ElasticSearch as Vector store when querying
|
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[] | null | 11 | 2023-08-07T04:58:11 | 2023-11-23T16:44:27 | null |
NONE
| null |
### System Info
Langchain version: `0.0.254`
Python version: `3.10.2`
Elasticsearch version: `7.17.0`
System Version: macOS 13.4 (22F66)
Model Name: MacBook Pro
Model Identifier: Mac14,10
Chip: Apple M2 Pro
Total Number of Cores: 12 (8 performance and 4 efficiency)
Memory: 32 GB
### Who can help?
@agola11 @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
run this using `python3 script.py`
`script.py`:
```python
import os
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch
def main():
text_path = "some-test.txt"
loader = TextLoader(text_path)
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
chunk_size=1000, chunk_overlap=0
) # I have also tried various chunk sizes, but still have the same error
documents = text_splitter.split_documents(data)
api_key = "..."
embeddings = OpenAIEmbeddings(openai_api_key=api_key)
os.environ["ELASTICSEARCH_URL"] = "..."
db = ElasticVectorSearch.from_documents(
documents,
embeddings,
index_name="laurad-test",
)
print(db.client.info())
db = ElasticVectorSearch(
index_name="laurad-test",
embedding=embeddings,
elasticsearch_url="..."
)
qa = RetrievalQA.from_chain_type(
llm=ChatOpenAI(temperature=0, openai_api_key=api_key),
chain_type="stuff",
retriever=db.as_retriever(),
)
query = "Hi
qa.run(query) # Error here
if __name__ == "__main__":
main()
```
Error traceback:
```
RequestError Traceback (most recent call last)
Cell In[8], line 2
1 query = "What is ARB in NVBugs?"
----> 2 qa.run(query)
File [/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/chains/base.py:451](https://file+.vscode-resource.vscode-cdn.net/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/chains/base.py:451), in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
449 if len(args) != 1:
450 raise ValueError("`run` supports only one positional argument.")
--> 451 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
452 _output_key
453 ]
455 if kwargs and not args:
456 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
457 _output_key
458 ]
File [/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/chains/base.py:258](https://file+.vscode-resource.vscode-cdn.net/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/chains/base.py:258), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
256 except (KeyboardInterrupt, Exception) as e:
257 run_manager.on_chain_error(e)
--> 258 raise e
259 run_manager.on_chain_end(outputs)
260 final_outputs: Dict[str, Any] = self.prep_outputs(
261 inputs, outputs, return_only_outputs
262 )
...
--> 315 raise HTTP_EXCEPTIONS.get(status_code, TransportError)(
316 status_code, error_message, additional_info
317 )
RequestError: RequestError(400, 'search_phase_execution_exception', "class_cast_exception: class org.elasticsearch.index.fielddata.ScriptDocValues$Doubles cannot be cast to class org.elasticsearch.xpack.vectors.query.VectorScriptDocValues$DenseVectorScriptDocValues (org.elasticsearch.index.fielddata.ScriptDocValues$Doubles is in unnamed module of loader 'app'; org.elasticsearch.xpack.vectors.query.VectorScriptDocValues$DenseVectorScriptDocValues is in unnamed module of loader java.net.FactoryURLClassLoader @7808fb9)")
```
### Expected behavior
I expect to ask questions and have answers provided back using the `langchain.chains.retrieval_qa.base.RetrievalQA` class. However, I am getting an error 400 when trying to query the LLM.
**Note**: I do not get the same error when using ChromaDB or OpenSearch as the retriever.
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| 1,838,637,509 |
PR_kwDOIPDwls5XSsZ4
| 8,848 |
Add BGE embeddings support
|
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[] | null | 4 | 2023-08-07T04:09:39 | 2023-08-07T18:15:30 | 2023-08-07T18:15:30 |
CONTRIBUTOR
| null |
- Description: [BGE-large](https://huggingface.co/BAAI/bge-large-en) embeddings from BAAI are at the top of [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Hence adding support for it.
- Tag maintainer: @baskaryan
- Twitter handle: @ManabChetia3
|
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I_kwDOIPDwls5tlyhU
| 8,846 |
Issue: ChromaDB document and token openAI limitations
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[] | null | 2 | 2023-08-07T03:48:48 | 2023-11-13T16:06:25 | 2023-11-13T16:06:24 |
NONE
| null |
### Issue you'd like to raise.
currently,
im using openAI GPT3.5 for models and chroma DB to save vector.
i have some pdf documents which is have 2000 total pages. the pages will increase about 100 pages every day.
and at the end, the total target page that must be inputed is around 20.000 pages.
i have 2 question:
1. i worry about token limitations from GPT3.5 . any idea / suggestion to improve token limitations for many source documents?
2. for chromaDB, are chromaDB has limitations for saving vector?
i already using text splitter
`textSplitter = RecursiveCharacterTextSplitter(chunk_size=1536, chunk_overlap=200,separators=["#####","\n\n","\n","====="])`
i accept all idea and suggestion for this, thanks for advice
please take a note: i cant make summary from every documents.
i accept all idea and suggestion for this, thanks for advice
please take a note: i cant make summary from every documents.
### Suggestion:
_No response_
|
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eugene/automaton variant 1
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[] | null | 2 | 2023-08-07T03:14:08 | 2023-09-20T17:44:17 | null |
COLLABORATOR
| null |
- x
- x
- x
- x
- x
- x
- x
- x
- x
- x
- x
- x
- x
- x
- x
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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| 1,838,564,650 |
PR_kwDOIPDwls5XSdEj
| 8,844 |
added filter kwarg to VectorStoreIndexWrapper query and query_with_so…
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[] | null | 2 | 2023-08-07T02:27:26 | 2023-08-08T17:27:55 | 2023-08-08T17:10:46 |
CONTRIBUTOR
| null |
- Description: added filter to query methods in VectorStoreIndexWrapper for filtering by metadata (i.e. search_kwargs)
- Tag maintainer: @rlancemartin, @eyurtsev
Updated the doc snippet on this topic as well. It took me a long while to figure out how to filter the vectorstore by filename, so this might help someone else out.
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I_kwDOIPDwls5tlkAR
| 8,843 |
ModuleNotFoundError: No module named 'langchain.schema'
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[] | null | 4 | 2023-08-07T02:25:35 | 2023-11-13T16:06:30 | 2023-11-13T16:06:29 |
NONE
| null |
langchain==0.0.20
from langchain.schema import messages_to_dict
----> 1 from langchain.schema import messages_to_dict
ModuleNotFoundError: No module named 'langchain.schema'
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I_kwDOIPDwls5tlMna
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TypeError Due to Duplicate 'auth' Argument in aiohttp Request when provide header to APIChain
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[] | null | 2 | 2023-08-06T23:55:31 | 2023-09-25T09:40:35 | 2023-09-25T09:40:35 |
NONE
| null |
### System Info
Langchain version: 0.0.253
Python:3.11
### Who can help?
@agola11 @hwchase17 @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
1. Environment Setup:
Ensure you're using Python 3.11.
Install the necessary libraries and dependencies:
```bash
pip install fastapi uvicorn aiohttp langchai
```
2. APIChain Initialization:
Set up the APIChain utility using the provided API documentation and the chosen language model:
```python
from langchain import APIChain
chain = APIChain.from_llm_and_api_docs(api_docs=openapi.MY_API_DOCS, llm=choosen_llm, verbose=True, headers=headers)
```
3. Run the FastAPI application:
Use a tool like Uvicorn to start your FastAPI app:
```lua
uvicorn your_app_name:app --reload
```
4. Trigger the API Endpoint:
Make a request to the FastAPI endpoint that uses the APIChain utility. This could be through tools like curl, Postman, or directly from a browser, depending on how your API is set up.
Execute the Callback:
Inside the relevant endpoint, ensure you have the following snippet:
```python
with get_openai_callback() as cb:
response = await chain.arun(user_query)
```
5. Observe the Error:
You should encounter a TypeError indicating a conflict with the auth argument in the aiohttp.client.ClientSession.request() method. Because of providing header to APIChain and running it with ```arun``` method.
### Expected behavior
Request Execution:
The chain.arun(user_query) method should interact with the intended external service or API without any issues.
The auth parameter, when used in the underlying request to the external service (in aiohttp), should be correctly applied without conflicts or multiple definitions.
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PR_kwDOIPDwls5XSH8A
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Add concurrency support for run_on_dataset
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[] | null | 1 | 2023-08-06T23:34:15 | 2023-08-07T16:24:50 | 2023-08-07T16:24:49 |
COLLABORATOR
| null |
Long-term, would be better to use the lower-level batch() method(s) but it may take me a bit longer to clean up. This unblocks in the meantime, though it may fail when the evaluated chain raises a `NotImplementedError` for a corresponding async method
|
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PR_kwDOIPDwls5XSFXu
| 8,840 |
Harrison/as retriever docstring
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PR_kwDOIPDwls5XR76K
| 8,839 |
fix exception inconsistencies (#8812)
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[] | null | 1 | 2023-08-06T20:43:18 | 2023-08-06T21:04:51 | 2023-08-06T21:04:50 |
COLLABORATOR
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Merge #8812 with main to fix unrelated test failure
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PR_kwDOIPDwls5XR7Vd
| 8,838 |
Add example ID to callback config
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| 1,838,372,136 |
PR_kwDOIPDwls5XR1Wu
| 8,837 |
Add Dist Metrics for String Distance Evaluation
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I_kwDOIPDwls5tksRM
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RecursionError: maximum recursion depth exceeded with ElasticVectorSearch during RetrievalQA
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[] | null | 2 | 2023-08-06T19:28:17 | 2023-08-06T21:40:49 | 2023-08-06T21:40:49 |
NONE
| null |
### System Info
Langchain version: `0.0.240`
Python version: `3.10.2`
Elasticsearch version: `7.17.0`
System Version: macOS 13.4 (22F66)
Model Name: MacBook Pro
Model Identifier: Mac14,10
Chip: Apple M2 Pro
Total Number of Cores: 12 (8 performance and 4 efficiency)
Memory: 32 GB
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [x] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
run this using `python3 script.py`
`script.py`:
```python
import os
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch
def main():
text_path = "some-test.txt"
loader = TextLoader(text_path)
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
chunk_size=1000, chunk_overlap=0
) # I have also tried various chunk sizes, but still have the same error
documents = text_splitter.split_documents(data)
api_key = "..."
embeddings = OpenAIEmbeddings(openai_api_key=api_key)
os.environ["ELASTICSEARCH_URL"] = "..."
db = ElasticVectorSearch.from_documents(
documents,
embeddings,
index_name="laurad-test",
)
print(db.client.info())
db = ElasticVectorSearch(
index_name="laurad-test",
embedding=embeddings,
elasticsearch_url="..."
)
qa = RetrievalQA.from_chain_type(
llm=ChatOpenAI(temperature=0, openai_api_key=api_key),
chain_type="stuff",
retriever=db.as_retriever(),
)
if __name__ == "__main__":
main()
```
Error traceback:
```
Traceback (most recent call last):
File "/Users/laurad/dev/LLM/public_test.py", line 46, in <module>
main()
File "/Users/laurad/dev/LLM/public_test.py", line 41, in main
retriever=db.as_retriever(),
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/vectorstores/base.py", line 458, in as_retriever
tags.extend(self.__get_retriever_tags())
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/vectorstores/base.py", line 452, in __get_retriever_tags
if self.embeddings:
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/vectorstores/elastic_vector_search.py", line 158, in embeddings
return self.embeddings
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/vectorstores/elastic_vector_search.py", line 158, in embeddings
return self.embeddings
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/vectorstores/elastic_vector_search.py", line 158, in embeddings
return self.embeddings
[Previous line repeated 993 more times]
RecursionError: maximum recursion depth exceeded
```
### Expected behavior
I expect to ask questions and have answers provided back using the `langchain.chains.retrieval_qa.base.RetrievalQA` class. However, I am getting a `RecursionError` when creating the retrieval chain.
**Note**: I do not get the same error when using ChromaDB or OpenSearch as the retriever.
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PR_kwDOIPDwls5XRm0E
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Add: USearch Vector Store
|
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[] | null | 2 | 2023-08-06T18:58:09 | 2023-08-08T03:41:01 | 2023-08-08T03:41:01 |
CONTRIBUTOR
| null |
## Description
I am excited to propose an integration with USearch, a lightweight vector-search engine available for both Python and JavaScript, among other languages.
## Dependencies
It introduces a new PyPi dependency - `usearch`. I am unsure if it must be added to the Poetry file, as this would make the PR too clunky. Please let me know.
## Profiles
- Maintainers: @ashvardanian @davvard
- Twitter handles: @ashvardanian @unum_cloud
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bump 254
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PR_kwDOIPDwls5XRkn7
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Bagatur/revert revert nuclia
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PR_kwDOIPDwls5XRkCy
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Revert "Bagatur/nuclia (#8404)"
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PR_kwDOIPDwls5XRihc
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Added chat history to codey models
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[] | null | 1 | 2023-08-06T17:33:33 | 2023-08-07T14:34:36 | 2023-08-07T14:34:36 |
CONTRIBUTOR
| null |
#7469
since 1.29.0, Vertex SDK supports a chat history provided to a codey chat model.
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PR_kwDOIPDwls5XRiXV
| 8,830 |
add example of memory and returning retrieved docs
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PR_kwDOIPDwls5XRhsq
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Adds Ollama as an LLM
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CONTRIBUTOR
| null |
Adds Ollama as an LLM. Ollama can run various open source models locally e.g. Llama 2 and Vicuna, automatically configuring and GPU-optimizing them.
@rlancemartin @hwchase17
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PR_kwDOIPDwls5XRe1L
| 8,828 |
Add Support for DingoDB as vectorstore
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[] | null | 4 | 2023-08-06T16:20:10 | 2023-08-11T17:54:56 | 2023-08-11T17:54:56 |
CONTRIBUTOR
| null |
Description:
[DingoDB](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases. It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
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