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PR_kwDOIPDwls5XI-kN
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Add improved sources splitting in BaseQAWithSourcesChain
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[] | null | 9 | 2023-08-03T19:45:53 | 2023-08-16T20:30:16 | 2023-08-16T20:30:15 |
CONTRIBUTOR
| null |
## Type:
Improvement
---
## Description:
Running QAWithSourcesChain sometimes raises ValueError as mentioned in issue #7184:
```
ValueError: too many values to unpack (expected 2)
Traceback:
response = qa({"question": pregunta}, return_only_outputs=True)
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\base.py", line 166, in __call__
raise e
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\base.py", line 160, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\qa_with_sources\base.py", line 132, in _call
answer, sources = re.split(r"SOURCES:\s", answer)
```
This is due to LLM model generating subsequent question, answer and sources, that is complement in a similar form as below:
```
<final_answer>
SOURCES: <sources>
QUESTION: <new_or_repeated_question>
FINAL ANSWER: <new_or_repeated_final_answer>
SOURCES: <new_or_repeated_sources>
```
It leads the following line
```
re.split(r"SOURCES:\s", answer)
```
to return more than 2 elements and result in ValueError. The simple fix is to split also with "QUESTION:\s" and take the first two elements:
```
answer, sources = re.split(r"SOURCES:\s|QUESTION:\s", answer)[:2]
```
Sometimes LLM might also generate some other texts, like alternative answers in a form:
```
<final_answer_1>
SOURCES: <sources>
<final_answer_2>
SOURCES: <sources>
<final_answer_3>
SOURCES: <sources>
```
In such cases it is the best to split previously obtained sources with new line:
```
sources = re.split(r"\n", sources.lstrip())[0]
```
---
## Issue:
Resolves #7184
---
## Maintainer:
@baskaryan
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
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PR_kwDOIPDwls5XI9sO
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Implement aadd_texts for pinecone vectorstore
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[] | null | 3 | 2023-08-03T19:42:20 | 2023-09-05T14:59:21 | 2023-09-05T14:59:21 |
NONE
| null |
## Summary
This PR implements the `async` `aadd_texts` method for the Pinecone vectorstore. It resolves https://github.com/langchain-ai/langchain/issues/8635.
## Context
Pinecone supports sending upsert requests in parallel, provided the index is initialized with `pool_threads > 1`, as per the example in their [documentation](https://docs.pinecone.io/docs/insert-data#sending-upserts-in-parallel).
This PR implements the `aadd_texts` defined by the `Base` vectorstore using the same parameters as the synchronous version. However, this implementation uses `batch_size` to chunk the embedded documents and unconditionally sets `async_req=True` for every `upsert` request. Additionally, we wrap the `result.get` calls to be called asyncrhonously. Note, once Python3.9+ is the base requirement, [`loop.run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) should be replaced by [`asyncio.to_thread`](https://docs.python.org/3/library/asyncio-task.html#asyncio.to_thread).
## Test Plan
```
omikader@Anh-This-Air langchain % poetry run pytest tests/integration_tests/vectorstores/test_pinecone.py
=========================================================================================== test session starts ============================================================================================
platform darwin -- Python 3.10.12, pytest-7.3.2, pluggy-1.0.0
rootdir: /Users/omikader/GitHub/langchain/libs/langchain
configfile: pyproject.toml
plugins: asyncio-0.20.3, cov-4.1.0, vcr-1.0.2, mock-3.11.1, syrupy-4.0.2, dotenv-0.5.2, anyio-3.7.0, socket-0.6.0
asyncio: mode=strict
collected 7 items
tests/integration_tests/vectorstores/test_pinecone.py ....... [100%]
============================================================================================= warnings summary =============================================================================================
.venv/lib/python3.10/site-packages/pkg_resources/__init__.py:121
/Users/omikader/GitHub/langchain/libs/langchain/.venv/lib/python3.10/site-packages/pkg_resources/__init__.py:121: DeprecationWarning: pkg_resources is deprecated as an API
warnings.warn("pkg_resources is deprecated as an API", DeprecationWarning)
.venv/lib/python3.10/site-packages/pkg_resources/__init__.py:2870
/Users/omikader/GitHub/langchain/libs/langchain/.venv/lib/python3.10/site-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.
Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
declare_namespace(pkg)
.venv/lib/python3.10/site-packages/deeplake/util/check_latest_version.py:32
/Users/omikader/GitHub/langchain/libs/langchain/.venv/lib/python3.10/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.15) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
warnings.warn(
tests/integration_tests/vectorstores/test_pinecone.py: 46 warnings
/Users/omikader/GitHub/langchain/libs/langchain/.venv/lib/python3.10/site-packages/pinecone/core/client/rest.py:45: DeprecationWarning: HTTPResponse.getheader() is deprecated and will be removed in urllib3 v2.1.0. Instead use HTTPResponse.headers.get(name, default).
return self.urllib3_response.getheader(name, default)
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
----------------------------------------------------------------------------------------- snapshot report summary ------------------------------------------------------------------------------------------
=========================================================================================== slowest 5 durations ============================================================================================
21.50s call tests/integration_tests/vectorstores/test_pinecone.py::TestPinecone::test_relevance_score_bound
5.81s call tests/integration_tests/vectorstores/test_pinecone.py::TestPinecone::test_aadd_texts
2.60s call tests/integration_tests/vectorstores/test_pinecone.py::TestPinecone::test_from_existing_index_with_namespaces
2.53s call tests/integration_tests/vectorstores/test_pinecone.py::TestPinecone::test_add_documents_with_ids
1.88s call tests/integration_tests/vectorstores/test_pinecone.py::TestPinecone::test_from_texts
===================================================================================== 7 passed, 49 warnings in 40.25s ======================================================================================
```
## Maintainers
Maintainer responsibilities:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Async: @agola11
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I_kwDOIPDwls5taYug
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Getting NameError: name 'partition_pdf' is not defined when running "documents = loader.load()"
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[] | null | 24 | 2023-08-03T19:40:00 | 2024-01-12T09:09:38 | null |
NONE
| null |
### System Info
I have been running this code for weeks, and today it looks like something changed to break it. I'm using the following code snippets...
from langchain.document_loaders import DirectoryLoader
...
loader = DirectoryLoader(directory_path, glob='**/*.pdf')
documents = loader.load()
This is the error I am getting...
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
[<ipython-input-9-8159690dd515>](https://localhost:8080/#) in <cell line: 2>()
1 loader = DirectoryLoader(directory_path, glob='**/*.pdf')
----> 2 documents = loader.load()
3 print("Number of documents: ", len(documents))
4
5 timestampit()
5 frames
[/usr/local/lib/python3.10/dist-packages/unstructured/partition/auto.py](https://localhost:8080/#) in partition(filename, content_type, file, file_filename, url, include_page_breaks, strategy, encoding, paragraph_grouper, headers, skip_infer_table_types, ssl_verify, ocr_languages, pdf_infer_table_structure, xml_keep_tags, data_source_metadata, **kwargs)
219 )
220 elif filetype == FileType.PDF:
--> 221 elements = partition_pdf(
222 filename=filename, # type: ignore
223 file=file, # type: ignore
NameError: name 'partition_pdf' is not defined
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
loader = DirectoryLoader(directory_path, glob='**/*.pdf')
documents = loader.load()
### Expected behavior
Loaded documents.
|
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relax deps for yaml
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[] | null | 1 | 2023-08-03T19:23:38 | 2023-08-03T20:22:18 | 2023-08-03T20:22:17 |
COLLABORATOR
| null |
context: https://github.com/yaml/pyyaml/issues/724
I think this is fine? I don't think we use yaml too heavily
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added minimum expected version of SDK to the error description
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[] | null | 2 | 2023-08-03T19:15:43 | 2023-08-03T20:28:49 | 2023-08-03T20:28:42 |
CONTRIBUTOR
| null |
#7932
|
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| 1,835,546,057 |
I_kwDOIPDwls5taDXJ
| 8,711 |
Cryptic error message trying to load NGramOverlapSelector
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[] | null | 3 | 2023-08-03T18:38:20 | 2023-11-10T16:06:47 | 2023-11-10T16:06:46 |
NONE
| null |
### System Info
langchain-0.0.251 pydantic-1.10.12
python 3.9
AWS Linux 6.1.38-59.109.amzn2023.x86_64
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] 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
from langchain.prompts.example_selector.ngram_overlap import NGramOverlapExampleSelector
from langchain.prompts import PromptTemplate
examples = [
dict(text="Call me Ishmael."),
dict(text="In a hole in the ground there lived a hobbit.")
]
example_prompt = PromptTemplate(input_variables=["text"], template="EXAMPLE: {text}")
selector = NGramOverlapExampleSelector(examples=examples, example_prompt=example_prompt)
t = selector.select_examples(dict(text="hole in ground"))
print(t)
### Expected behavior
Should print the selected examples. Instead I get:
```
Traceback (most recent call last):
File "/work/2023/08/partial/src/ngram_demo.py", line 12, in <module>
selector = NGramOverlapExampleSelector(examples=examples, example_prompt=example_prompt)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for NGramOverlapExampleSelector
__root__
Not all the correct dependencies for this ExampleSelect exist (type=value_error)
```
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| 1,835,528,046 |
I_kwDOIPDwls5tZ-9u
| 8,710 |
Issue: Issue providing LLMChain with multiple variables when memory is used
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[] | null | 7 | 2023-08-03T18:24:14 | 2023-12-20T16:07:01 | 2023-12-20T16:07:00 |
NONE
| null |
### Issue you'd like to raise.
I'm having an issue with providing the LLMChain class with multiple variables when I provide it with a memory object. It works fine when I don't have memory attached to it. I followed the example given in this document: [LLM Chain Multiple Inputs](https://python.langchain.com/docs/modules/chains/foundational/llm_chain#additional-ways-of-running-llm-chain)
Here is the code that I used, which is mostly based on the example from the above documentation, besides I've added memory.
```
# Multiple inputs example
template = """Tell me a {adjective} joke about {subject}."""
prompt = PromptTemplate(template=template, input_variables=["adjective", "subject"])
llm = OpenAI(temperature=0)
memory = ConversationKGMemory(llm=llm)
llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))
llm_chain.predict(adjective="sad", subject="ducks")
```
With the above code, I get the following error:
```
ValueError: One input key expected got ['adjective', 'subject']
```
Python version: 3.11
LangChain version: 0.0.250
### Suggestion:
Is there support for using multiple input variables when memory is involved?
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| 1,835,500,105 |
PR_kwDOIPDwls5XIhWK
| 8,709 |
(issue #5163) Append reminder to nest multi-prompt router prompt output in JSON markdown code block, resolving JSON parsing error.
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[] | null | 2 | 2023-08-03T18:01:22 | 2023-08-04T19:36:34 | 2023-08-04T19:36:34 |
CONTRIBUTOR
| null |
Resolves occasional JSON parsing error when some predictions are passed through a `MultiPromptChain`.
Makes [this modification](https://github.com/langchain-ai/langchain/issues/5163#issuecomment-1652220401) to `multi_prompt_prompt.py`, which is much cleaner than appending an entire example object, which is another community-reported solution.
@hwchase17, @baskaryan
cc: @SimasJan
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I_kwDOIPDwls5tZubB
| 8,708 |
Allow GPT4AllEmbeddings() constructor to be configured
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[] | null | 3 | 2023-08-03T17:30:19 | 2023-12-19T12:59:54 | null |
NONE
| null |
### Feature request
I would be great to be able to pass in configuration info to the `Embeddings` from the `GTP4AllEmbeddings()` constructor in the same manner as the `GPT4All()` constructor. For example. To be able to turn of allow_download, model, etc..
`embeddings = GPT4AllEmbeddings(allow_download=False)`
### Motivation
It would allow for the same level of customization as the `GPT4All()` constructor. Right now there is no way to run FAISS in completely offline mode. Trying to do so will hang and error out with the models lookup call to https://gpt4all.io/models/models.json. Being able to pass in configuration information would fix issues such as these. As it is right now, I have to download the gpt4all repo and manually patch the `Embeddings` to pass in the configuration I need.
### Your contribution
Probably not at this time. I would love to, but I'm a bit too busy with work at the moment.
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| 8,706 |
Deterministic Fake Embedding Model
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[] | null | 2 | 2023-08-03T17:07:07 | 2023-08-03T20:36:46 | 2023-08-03T20:36:46 |
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),
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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:
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
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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
-->
Solves #8644
This embedding models output identical random embedding vectors, given the input texts are identical.
Useful when used in unittest.
@baskaryan
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PR_kwDOIPDwls5XIRA0
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feat: Add Spell Correction Spec to Google Cloud Enterprise Search connector
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CONTRIBUTOR
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PR_kwDOIPDwls5XIPx7
| 8,704 |
Add tensor_split support to LlamaCpp
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NONE
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Adds the option `tensor_split` to the `LlamaCpp` binding, for specifying how to split the model memory onto multiple GPUs.
Example running airoboros 33b on 4x Nvidia RTX 2080Ti, (11 GB each)
```python
os.environ["CUDA_VISIBLE_DEVICES"]="0,1,2,3"
LlamaCpp(
model_path="airoboros-33b-gpt4-2.0.ggmlv3.q8_0.bin",
n_gpu_layers=63,
tensor_split=[0.1, 0.3, 0.3, 0.3],
...
```
This leaves space on GPU0 for the tokenizer etc.
More info about the option in the [llama-cpp-python documentation](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.llama.Llama.__init__).
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PR_kwDOIPDwls5XIJJ3
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Create pylint.yml
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[] | null | 6 | 2023-08-03T16:34:25 | 2023-09-20T17:42:11 | 2023-09-20T17:42:11 |
NONE
| null |
I suggest incorporating Pylint into your workflow to maintain a clean architecture for the codebase.
cc: @baskaryan
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lint
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PR_kwDOIPDwls5XIHX1
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wip -- high temp
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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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PR_kwDOIPDwls5XH80_
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Updates to Vectara documentation
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[] | null | 1 | 2023-08-03T15:53:54 | 2023-08-04T03:21:17 | 2023-08-04T03:21:17 |
CONTRIBUTOR
| null |
- Description: updates to Vectara documentation with more details on how to get started.
- Issue: NA
- Dependencies: NA
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @vectara, @ofermend
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PR_kwDOIPDwls5XH47l
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Support evaluating runnables and arbitrary functions
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[] | null | 2 | 2023-08-03T15:42:11 | 2023-08-04T23:39:06 | 2023-08-04T23:39:05 |
COLLABORATOR
| null |
Added a couple of "integration tests" for these that I ran.
Main design point of feedback: at this point, would it just be better to have separate arguments for each type? Little confusing what is or isn't supported and what is the intended usage at this point since I try to wrap the function as runnable or pack or unpack chains/llms.
```
run_on_dataset(
...
llm_or_chain_factory = None,
llm = None,
chain = NOne,
runnable=None,
function=None
):
# raise error if none set
```
Downside with runnables and arbitrary function support is that you get much less helpful validation and error messages, but I don't think we should block you from this, at least.
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PR_kwDOIPDwls5XHtPC
| 8,696 |
Permit none key in chain mapper
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[] | null | 1 | 2023-08-03T15:08:50 | 2023-08-03T15:50:37 | 2023-08-03T15:50:36 |
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I_kwDOIPDwls5tY0eB
| 8,695 |
ValueError: ChatAgent does not support multi-input tool do_my_job. with a non-argaument function
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[] | null | 3 | 2023-08-03T14:51:14 | 2023-11-10T16:06:51 | 2023-11-10T16:06:51 |
NONE
| null |
### System Info
OS: Linux Mint 21.2 Cinnamon
Python 3.10
LangChain v0.0.251
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I defined a function like:
```python
@tool
def do_my_job() -> list:
"""Just do my job"""
return []
```
Obviously it has none of arguments.
After I made an agent and run:
```python
lc_tools = [
tool.do_my_job
]
lc_agent_executor = initialize_agent(agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, llm=lc_llm, tools=lc_tools,
verbose=config.verbose)
lc_reply = lc_agent_executor.run("do my job")
```
I got this error: `ValueError: ChatAgent does not support multi-input tool do_my_job.`, which raised at `langchain.agents.utils.py`:
```python
def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None:
"""Validate tools for single input."""
for tool in tools:
if not tool.is_single_input:
raise ValueError(
f"{class_name} does not support multi-input tool {tool.name}."
)
```
When the function `do_my_job` has arguments = 1, the `tool.is_single_input` was `True`
When the function `do_my_job` has arguments > 1, the `tool.is_single_input` was `False`
When the function `do_my_job` has arguments < 1, the `tool.is_single_input` was `False`
I can specify an unused argument for the function, but it is a bit weird.
Should this check be correct?
### Expected behavior
Non-argument functions should work with **Zero-shot ReAct**
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| 8,694 |
RSS Feed / OPML loader
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CONTRIBUTOR
| null |
Replace this comment with:
- Description: added a document loader for a list of RSS feeds or OPML. It iterates through the list and uses NewsURLLoader to load each article.
- Issue: N/A
- Dependencies: feedparser, listparser
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @ruze
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I_kwDOIPDwls5tYTrJ
| 8,691 |
Continuing LLM response parsing problems after 250(251)
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[] | null | 9 | 2023-08-03T13:39:39 | 2023-12-14T16:22:26 | 2023-12-03T16:05:20 |
NONE
| null |
### System Info
ubuntu
[v0.0.250](https://github.com/langchain-ai/langchain/releases/tag/v0.0.250)
### Who can help?
@hwchase17 @agola11 @hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
1. implement multi input structured tool
2. enter user input to trigger the tool
3. check parsing
have had a chance to read and review earlier related regex and other parsing issues filed earlier some of which were marked as solved with 249 and 250 but continuing to have parsing issues
for example
https://github.com/langchain-ai/langchain/pull/7511
which is merged
when invoking a multi input structured tool
LLM returns
```
> Entering new AgentExecutor chain...
Could not parse LLM output: Action:
```json
{
"action": "image_generate",
"action_input": {
"prompt": "Draw a cat"
}
```
with extra 'json' before the valid json which is then cannot parse
2. have tried to handle this in the system prompt with (one example but tried multiple):
```
_SUFFIX = ("Don't ask the user to confirm if you are pretty sure what you need to do in order to fulfill the request. "
"For example, don't ask the user 'I can DO X. Would you like me to do that?', just do it. "
"Make sure that any JSON you return is fully valid, does not include any extra 'json' text before the JSON object. "
"For example, If you see you are generating a JSON that looks like this"
"```json"
"{{\"action\": \"edit_image\",\"action_input\": {{\"image_path\": \"sandbox:/images/edited_from_image_a1bc6dbb-1127-49d5-898a-d35c3bb1564d.png\",\"prompt\": \"Make the drawing even more basic\"}}}}"
"``` and has the text 'json' before the valid JSON object, remove the extra 'json' text at the start so that your final output is ```"
"{{\"action\": \"edit_image\",\"action_input\": {{\"image_path\": \"sandbox:/images/edited_from_image_a1bc6dbb-1127-49d5-898a-d35c3bb1564d.png\",\"prompt\": \"Make the drawing even more basic\"}}}}"
"``` without the json"
"Chat history:\n{chat_history}\n\n") + _SUFFIX
```
any help in trying to either parse better or instruct the LLM to provide a better answer would be appreciated.
thank in advance.
### Expected behavior
either
1) works out of the box with another version of the
https://github.com/langchain-ai/langchain/pull/7511
resolution
2) works with additional LLM system prompt SUFFIX instructions if need be.
have tried a bunch of variations can't get it to work,
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| 1,835,053,403 |
PR_kwDOIPDwls5XHCEJ
| 8,690 |
bump 251
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[] | null | 1 | 2023-08-03T13:19:41 | 2023-08-03T13:28:37 | 2023-08-03T13:28:36 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5XHB9E
| 8,689 |
updated interface jupyter notebook explanations
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[] | null | 2 | 2023-08-03T13:19:24 | 2023-08-03T15:53:31 | 2023-08-03T15:53:31 |
CONTRIBUTOR
| null |
Updated the documentation in the interface.ipynb to clearly show the _input_ and _output_ types for various components @baskaryan
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I_kwDOIPDwls5tX4Md
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text_embedding with OpenAIEmbeddings cannot work
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[] | null | 4 | 2023-08-03T12:34:16 | 2023-11-17T16:06:24 | 2023-11-17T16:06:23 |
NONE
| null |
**System Info**
python=3.10.8
langchain = 0.0.250
openai=0.27.8
**Information**
The official example notebooks/scripts
The error occurs when I run the official example:
https://python.langchain.com/docs/integrations/text_embedding/azureopenai
I use the azure open, and the code are shown in following:
embeddings = OpenAIEmbeddings(
deployment="text-davinci-002",
openai_api_key=os.environ["OPENAI_API_KEY"],
openai_api_type="azure",
openai_api_base=os.environ["OPENAI_API_BASE"],
openai_api_version=os.environ["OPENAI_API_VERSION"]
)
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
and the error is:
Traceback (most recent call last):
File "./inter.py", line 54, in <module>
query_result = embeddings.embed_query(text)
File "./miniconda3/lib/python3.10/site-packages/langchain/embeddings/openai.py", line 500, in embed_query
return self.embed_documents([text])[0]
File "./miniconda3/lib/python3.10/site-packages/langchain/embeddings/openai.py", line 472, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
File "./miniconda3/lib/python3.10/site-packages/langchain/embeddings/openai.py", line 358, in _get_len_safe_embeddings
response = embed_with_retry(
File "./miniconda3/lib/python3.10/site-packages/langchain/embeddings/openai.py", line 107, in embed_with_retry
return _embed_with_retry(**kwargs)
File "./miniconda3/lib/python3.10/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "./miniconda3/lib/python3.10/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "./miniconda3/lib/python3.10/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
File "./miniconda3/lib/python3.10/concurrent/futures/_base.py", line 451, in result
return self.__get_result()
File "./miniconda3/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "./miniconda3/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "./miniconda3/lib/python3.10/site-packages/langchain/embeddings/openai.py", line 104, in _embed_with_retry
response = embeddings.client.create(**kwargs)
File "./miniconda3/lib/python3.10/site-packages/openai/api_resources/embedding.py", line 33, in create
response = super().create(*args, **kwargs)
File "./miniconda3/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
File "./miniconda3/lib/python3.10/site-packages/openai/api_requestor.py", line 298, in request
resp, got_stream = self._interpret_response(result, stream)
File "./miniconda3/lib/python3.10/site-packages/openai/api_requestor.py", line 700, in _interpret_response
self._interpret_response_line(
File "./miniconda3/lib/python3.10/site-packages/openai/api_requestor.py", line 763, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again.
I have check the issues, while this problem cannot be solved with the following solutions:
- solutions mentioned in #4819
- adding chunk_size=1
- only use OpenAIEmbeddings()
### Suggestion:
_No response_
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I_kwDOIPDwls5tX206
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AttributeError: module 'cohere' has no attribute 'AsyncClient'
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[] | null | 2 | 2023-08-03T12:30:40 | 2023-11-09T16:12:29 | 2023-11-09T16:12:28 |
NONE
| null |
### System Info
langchain 0.0.250
langchainplus-sdk 0.0.20
Python 3.8.10
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain import Cohere
llm = Cohere(temperature=0.9, cohere_api_key=cohere_api_key)
### Expected behavior
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 llm = Cohere(temperature=0.9, cohere_api_key=cohere_api_key)
File /opt/miniconda3/envs/ai/lib/python3.8/site-packages/langchain/load/serializable.py:74, in Serializable.__init__(self, **kwargs)
73 def __init__(self, **kwargs: Any) -> None:
---> 74 super().__init__(**kwargs)
75 self._lc_kwargs = kwargs
File /opt/miniconda3/envs/ai/lib/python3.8/site-packages/pydantic/main.py:339, in pydantic.main.BaseModel.__init__()
File /opt/miniconda3/envs/ai/lib/python3.8/site-packages/pydantic/main.py:1102, in pydantic.main.validate_model()
File /opt/miniconda3/envs/ai/lib/python3.8/site-packages/langchain/llms/cohere.py:127, in Cohere.validate_environment(cls, values)
124 import cohere
126 values["client"] = cohere.Client(cohere_api_key)
--> 127 values["async_client"] = cohere.AsyncClient(cohere_api_key)
128 except ImportError:
129 raise ImportError(
130 "Could not import cohere python package. "
131 "Please install it with `pip install cohere`."
132 )
AttributeError: module 'cohere' has no attribute 'AsyncClient'
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PR_kwDOIPDwls5XGmxA
| 8,685 |
Add BasePromptTemplate.formatters which act on input variables before they are passed to template string
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COLLABORATOR
| null |
Default formatters for list, dict and Document
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
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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!
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
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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_kwDOIPDwls5XGatO
| 8,684 |
Fixed error handle null value in csv file
|
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[] | null | 1 | 2023-08-03T11:28:35 | 2023-08-04T10:39:10 | 2023-08-04T10:39:10 |
NONE
| null |
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Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
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- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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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_kwDOIPDwls5XGV40
| 8,683 |
Small improvements for tracer and debug output of runnables
|
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[] | null | 3 | 2023-08-03T11:14:03 | 2023-08-10T06:24:13 | 2023-08-10T06:24:12 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
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If you're adding a new integration, please include:
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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
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- 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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| 1,834,821,607 |
PR_kwDOIPDwls5XGO78
| 8,682 |
Make json output parser handle newlines inside markdown code blocks
|
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[] | null | 2 | 2023-08-03T10:54:53 | 2023-08-07T22:49:54 | 2023-08-07T22:49:54 |
CONTRIBUTOR
| null |
Update to #8528
Newlines and other special characters within markdown code blocks returned as `action_input` should be handled correctly (in particular, unescaped `"` => `\"` and `\n` => `\\n`) so they don't break JSON parsing.
@baskaryan
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| 1,834,819,131 |
I_kwDOIPDwls5tXR47
| 8,681 |
Issue with Langchain: Error When Implementing ConstitutionalChain with RetrievalQaChain and Vecatra
|
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[] | null | 2 | 2023-08-03T10:53:12 | 2023-11-09T16:07:31 | 2023-11-09T16:07:30 |
NONE
| null |
### System Info
I am currently working with the Langchain platform and I've encountered an issue during the integration of ConstitutionalChain with the existing retrievalQaChain.
In my implementation, I've used retrievalQaChain with a custom prompt and Vecatra as the retriever. My system is hosted on Vercel and I'm primarily aiming to acquire a streaming output.
My objective was to utilize ConstitutionalChain alongside retrievalQaChain to improve the system's overall performance. However, upon integrating ConstitutionalChain, I am continually facing an error that disrupts the seamless functioning of my system.
Regrettably, I've not been able to identify the root cause or find a solution for this persistent issue. It would be highly beneficial if I could receive assistance to overcome this obstacle. To provide better context and aid the debugging process, I'll follow up with the specifics of the error message, the steps that lead to the error, and any relevant screenshots if possible.
**Error: error [TypeError: Cannot read properties of undefined (reading 'format')]**
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
const model = new ChatOpenAI({
temperature: 0,
azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION,
azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME,
azureOpenAIApiDeploymentName:
process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,
streaming: true,
callbackManager: CallbackManager.fromHandlers(handlers),
});
const metadataFilter = `doc.sourceId=${SourceId}`;
const retrivalChain = new RetrievalQAChain({
combineDocumentsChain: loadQAStuffChain(model, { prompt }),
retriever: vectorStore.asRetriever(10, { filter: metadataFilter }),
returnSourceDocuments: true,
});
const principle = new ConstitutionalPrinciple({
name: 'Ethical Principle',
critiqueRequest:
'The model should only talk about ethical and legal things.',
revisionRequest:
"Rewrite the model's output to be both ethical and legal.",
});
const chain = ConstitutionalChain.fromLLM(model, {
chain: retrivalChain,
constitutionalPrinciples: [principle],
});
chain
.call({
query,
})
.then((result) => {
// TODO: consume the references
// eslint-disable-next-line no-console
console.log('result.sourceDocuments', result.sourceDocuments); // This will log the source documents
})
.catch((e) => {
// eslint-disable-next-line no-console
console.error('error', e.message);
})
.finally(() => {
handlers.handleChainEnd();
});
const corsHeaders = {
'Access-Control-Allow-Origin': '*',
};
return new StreamingTextResponse(stream, {
headers: corsHeaders,
});
### Expected behavior
It should return the streamed output with the constitutional principle
|
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I_kwDOIPDwls5tXGLQ
| 8,680 |
LangChain 0.0.250 circular import bug, possibly LangSmith related
|
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[] | null | 2 | 2023-08-03T10:21:15 | 2023-08-03T19:40:14 | 2023-08-03T19:40:14 |
NONE
| null |
### System Info
There seems to be a circular import problem introduced with `langsmith` dependency.
I believe so, because this problem is constantly appearing in the `langchain` versions that do include `langsmith` dependency, and for example `0.0.230` seems to be ok (I don't see that one including `langsmith` as dep), but latter versions are exhibiting the same problem. And there is also `langchain` / `langsmith` back-fourth in stacktrace.
The error:
```text
Traceback (most recent call last):
File "/Users/Dzmitry_Kankalovich/Workspace/personal/langsmith.py", line 2, in <module>
from langchain.chat_models import ChatOpenAI
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 31, in <module>
from langchain.agents.agent import (
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 15, in <module>
from langchain.agents.agent_iterator import AgentExecutorIterator
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/agents/agent_iterator.py", line 21, in <module>
from langchain.callbacks.manager import (
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/callbacks/__init__.py", line 21, in <module>
from langchain.callbacks.manager import (
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/callbacks/manager.py", line 41, in <module>
from langchain.callbacks.tracers.langchain import LangChainTracer
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/callbacks/tracers/__init__.py", line 3, in <module>
from langchain.callbacks.tracers.langchain import LangChainTracer
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/callbacks/tracers/langchain.py", line 11, in <module>
from langsmith import Client
File "/Users/Dzmitry_Kankalovich/Workspace/personal/langsmith.py", line 2, in <module>
from langchain.chat_models import ChatOpenAI
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/chat_models/__init__.py", line 20, in <module>
from langchain.chat_models.anthropic import ChatAnthropic
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/chat_models/anthropic.py", line 3, in <module>
from langchain.callbacks.manager import (
ImportError: cannot import name 'AsyncCallbackManagerForLLMRun' from partially initialized module 'langchain.callbacks.manager' (most likely due to a circular import) (/Users/Dzmitr
y_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/callbacks/manager.py)
```
### Who can help?
@hwchase17
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
0. Use python `3.11`
1. Take sample *LangSmith* code from their tutorial:
```py
import os
from langchain.chat_models import ChatOpenAI
os.environ['LANGCHAIN_TRACING_V2'] = 'true'
os.environ['LANGCHAIN_ENDPOINT'] = "https://api.smith.langchain.com"
llm = ChatOpenAI()
llm.predict("Hello, world!")
```
2. Provision fresh `venv` with the following dependencies:
```text
langchain==0.0.250
openai==0.27.8
```
3. Observe stacktrace:
```text
from langchain.callbacks.tracers.langchain import LangChainTracer
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/callbacks/tracers/__init__.py", line 3, in <module>
from langchain.callbacks.tracers.langchain import LangChainTracer
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/callbacks/tracers/langchain.py", line 11, in <module>
from langsmith import Client
File "/Users/Dzmitry_Kankalovich/Workspace/personal/langsmith.py", line 2, in <module>
from langchain.chat_models import ChatOpenAI
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/chat_models/__init__.py", line 20, in <module>
from langchain.chat_models.anthropic import ChatAnthropic
File "/Users/Dzmitry_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/chat_models/anthropic.py", line 3, in <module>
from langchain.callbacks.manager import (
ImportError: cannot import name 'AsyncCallbackManagerForLLMRun' from partially initialized module 'langchain.callbacks.manager' (most likely due to a circular import) (/Users/Dzmitr
y_Kankalovich/Workspace/personal/.venv/lib/python3.11/site-packages/langchain/callbacks/manager.py)
```
### Expected behavior
No errors, llm reply received
|
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| 1,834,615,597 |
I_kwDOIPDwls5tWgMt
| 8,679 |
Handle duplicate IDs error when using langchain.vectorstores.Chroma.from_documents
|
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[] | null | 1 | 2023-08-03T09:07:10 | 2023-11-14T16:07:04 | 2023-11-14T16:07:03 |
CONTRIBUTOR
| null |
### System Info
Related [Stackoverflow](https://stackoverflow.com/questions/76414862/how-do-you-catch-the-duplicate-id-error-when-using-langchain-vectorstores-chroma) post. Is there any way to avoid duplicated IDs and only insert new ones.
@agola11
### 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
```python
Chroma.from_documents(docs, embeddings, ids=ids, persist_directory='db')
```
### Expected behavior
Non-duplicate IDs get added
|
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I_kwDOIPDwls5tWYR9
| 8,678 |
Unable to import Html2TextTransformer from langchain.document_transformers
|
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[] | null | 2 | 2023-08-03T08:47:29 | 2023-11-09T16:10:19 | 2023-11-09T16:10:18 |
NONE
| null |
### System Info
langchain==0.0.250
Experiencing error when importing Html2TextTransformer
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[79], line 1
----> 1 from langchain.utils.math import cosine_similarity
ModuleNotFoundError: No module named 'langchain.utils.math'; 'langchain.utils' is not a package
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_transformers import Html2TextTransformer
### Expected behavior
Importing successfully.
|
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I_kwDOIPDwls5tWWf-
| 8,677 |
DOC: What is the difference between LOTR(Merger Retriever) and Ensemble Retriever
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[] | null | 1 | 2023-08-03T08:44:25 | 2023-08-07T10:08:04 | 2023-08-07T10:08:04 |
NONE
| null |
### Issue with current documentation:
https://python.langchain.com/docs/integrations/retrievers/merger_retriever
https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble
seems like the same.
Both task a list of exist retriever and MERGE the output together.
### Idea or request for content:
Should describe the difference between the both.
|
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I_kwDOIPDwls5tWUq2
| 8,676 |
what is the difference between the combine agents &vector stores and vectorstore agent?
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[] | null | 3 | 2023-08-03T08:40:56 | 2023-11-15T16:07:22 | 2023-11-15T16:07:21 |
NONE
| null |
combine agents &vector stores - https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore
vectorstore agent - https://python.langchain.com/docs/integrations/toolkits/vectorstore
what is the diffeence between these two?
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I_kwDOIPDwls5tWLQM
| 8,675 |
Failed to calculate number of tokens, falling back to approximate count
|
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[] | null | 5 | 2023-08-03T08:17:16 | 2023-11-12T16:06:29 | 2023-11-12T16:06:28 |
NONE
| null |
### System Info
Hi guys, I wanted to ask you does anyone know where is the problem with this because Im getting an error: (Failed to calculate number of tokens, falling back to approximate count) after trying to make a request to open ai by using langchain and sql database connection. On the request I can see that there is a property with max_tokens: 256, is there a problem with the api key or pricing plan or anything related to the database size?
The version of langchain library that Im using is "langchain": "0.0.110",
### 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
- [x] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Im trying to chat with my database and Im getting the error above.
### Expected behavior
It is showing me an error (Failed to calculate number of tokens, falling back to approximate count) and it is taking so much time to load.
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| 1,834,500,944 |
I_kwDOIPDwls5tWENQ
| 8,674 |
RunError of langchain's tutorial in deeplearning.ai, L3-Chains
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[] | null | 3 | 2023-08-03T07:58:27 | 2023-11-14T16:06:44 | 2023-11-14T16:06:43 |
NONE
| null |
### Issue you'd like to raise.
I learn the langchain lessions, and found some error while running the jupyter code at
`https://learn.deeplearning.ai/langchain/lesson/4/chains`,
precisily, MULTI_PROMPT_ROUTER_TEMPLATE is not working well in the first run,
1）It raise the folloing error,
```
OutputParserException: Parsing text
{
"destination": "physics",
"next_inputs": "What is black body radiation?"
}
raised following error:
Got invalid return object. Expected markdown code snippet with JSON object, bu
```
Then I add additional example I/O (after <<OUTPUT>>) for it,
```
eg:
<< INPUT >>
"What is black body radiation?"
<< OUTPUT >>
\```json
{{{{
"destination": string \ name of the prompt to use or "DEFAULT"
"next_inputs": string \ a potentially modified version of the original input
}}}}
\```
```
It works, but still error at routing to default path as I ask it "Why does every cell in our body contain DNA?"
2) It tried to route to biology, but failed to get response.
```
ValueError: Received invalid destination chain name 'biology'
```
How can I get stable running result? Or,this issue is one of the flaws of current llm, which cannot ensure that all outputs are processed according to our specified style?
Thanks a lot.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5tV1c9
| 8,673 |
What does langchain's logo mean
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[] | null | 3 | 2023-08-03T07:17:23 | 2023-12-02T07:37:01 | 2023-10-30T21:18:05 |
NONE
| null |
### Issue you'd like to raise.
What does langchain's logo mean
### Suggestion:
_No response_
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| 1,834,425,326 |
I_kwDOIPDwls5tVxvu
| 8,672 |
Getting model args exception while using ManifestWrapper
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[] | null | 2 | 2023-08-03T07:07:08 | 2023-11-09T16:12:42 | 2023-11-09T16:12:42 |
CONTRIBUTOR
| null |
### System Info
Hi, I'm trying to interact with HuggingFace model (NumbersStation/nsql-llama-2-7B) using manifest but when I try to to send it a prompt I'm getting the following error from the model:
`The following 'model_kwargs' are not used by the model: ['token_type_ids'] (note: typos in the generate arguments will also show up in this list)`
I tried to find out how to control the mode_kwargs in the ManifestWarpper but didn't find such.
That's how I configured the wrapper:
```
local_llm = ManifestWrapper(
client=manifest,
llm_kwargs={"temperature": 0.0, "max_tokens": 1024},
verbose=True
)
```
and that's how I ran the model using Manifest (on my local machine):
```
python3 -m manifest.api.app \
--model_type huggingface \
--model_generation_type text-generation \
--model_name_or_path nsql-llama-2-7B \
--device 0
```
Would like some help here :)
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Run the model locally using Manifest
2. connect to it using ManifestWrapper.
3. send a simple prompt with LLMChain.
### Expected behavior
Getting response from the model.
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I_kwDOIPDwls5tVxg7
| 8,671 |
how to set chat_history for different user
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[] | null | 4 | 2023-08-03T07:06:27 | 2023-11-30T08:19:42 | 2023-08-05T06:44:30 |
NONE
| null |
### Issue you'd like to raise.
I just want to set chat history for different user in ConversationBufferMemory, user can only get his own chathistory
this is my code：
**embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1000)
docsearch = Chroma.from_documents(split_docs, embeddings)
memory = ConversationBufferMemory(memory_key="chat_history")
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="map_rerank",
retriever=docsearch.as_retriever(),memory=memory, return_source_documents=False)**
could anyone give me some advice please
### Suggestion:
_No response_
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PR_kwDOIPDwls5XE3mQ
| 8,670 |
Bagatur/eden llm
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| null | null |
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add model_revison parameter to ModelScopeEmbeddings
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[] | null | 2 | 2023-08-03T07:02:49 | 2023-08-03T21:17:53 | 2023-08-03T21:17:49 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add model_revison parameter to ModelScopeEmbeddings,
- 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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| 1,834,353,463 |
I_kwDOIPDwls5tVgM3
| 8,667 |
Issue: < Emails are not being sent though it shows emails sent successfully >
|
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[] | null | 1 | 2023-08-03T06:18:56 | 2023-08-03T09:44:12 | 2023-08-03T09:44:12 |
NONE
| null |
### Issue you'd like to raise.
I am trying to send emails using the `ZapierNLAWrapper`. Though it shows emails have been sent successfully, I could not find sent emails in gmail.
```
from langchain.chains import TransformChain
from langchain.tools.zapier.tool import ZapierNLARunAction
from langchain.utilities.zapier import ZapierNLAWrapper
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.agents import AgentType
zapier = ZapierNLAWrapper(zapier_nla_api_key="")
actions = zapier.list()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(toolkit.get_tools(), llm, agent="zero-shot-react-description", verbose=True)
agent.run(
f"The emails of the shortlisted candidates are the following: [email protected] and [email protected]. Their names are Arghya and Nilesh respectively. Send emails to them, In the body of the email, congratulate them on their selection and inform them about the next steps in the hiring process."
)
```
Here is the Verbose log:
```
> Entering new AgentExecutor chain...
I need to send emails to the shortlisted candidates. I should use the Gmail: Send Email tool.
Action: Gmail: Send Email
Action Input:
- Subject: "Congratulations on Your Selection"
- Body: "Dear [Candidate's Name],\n\nCongratulations on being shortlisted for the position! We are pleased to inform you that you have been selected to proceed to the next steps in the hiring process. We will be contacting you shortly with more details.\n\nBest regards,\n[Your Name]"
- To: "[email protected]"
- Cc: "[email protected]"
Observation: null
Thought:The email has been sent to the first candidate. I need to send the email to the second candidate now.
Action: Gmail: Send Email
Action Input:
- Subject: "Congratulations on Your Selection"
- Body: "Dear [Candidate's Name],\n\nCongratulations on being shortlisted for the position! We are pleased to inform you that you have been selected to proceed to the next steps in the hiring process. We will be contacting you shortly with more details.\n\nBest regards,\n[Your Name]"
- To: "[email protected]"
- Cc: "[email protected]"
Observation: null
Thought:Both emails have been sent successfully.
Final Answer: The emails have been sent to the shortlisted candidates.
> Finished chain.
```
### Suggestion:
_No response_
|
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I_kwDOIPDwls5tVYSc
| 8,666 |
Redis Cache to support ChatModel
|
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[] | null | 3 | 2023-08-03T05:50:39 | 2023-11-27T16:08:11 | 2023-11-27T16:08:11 |
NONE
| null |
### Feature request
I'm writing to propose the integration of Redis Cache support for ChatModels within Langchain. Chat functionality is an essential and perhaps the most common scenario in many applications. Enhancing Langchain with Redis Cache support for ChatModels can bring about the following benefits.
### Motivation
1. Performance Improvement:
Utilizing Redis Cache can significantly reduce the latency, providing users with a faster and more responsive chat experience.
Caching chat messages and related data can lower the database load and enhance overall system performance.
2. Scalability:
Redis Cache can enable Langchain to efficiently manage large-scale real-time chat systems.
The support for various data structures in Redis enables sophisticated caching strategies, accommodating different chat scenarios and requirements.
3. Reliability:
Redis's replication and persistence features can offer a reliable caching layer that ensures data consistency and availability.
It supports various eviction policies that allow for a flexible and efficient utilization of memory.
4. Integration Ease:
Redis is widely used, well-documented, and has client libraries in most programming languages, making integration with Langchain straightforward.
The community around Redis is vibrant and active, which can be beneficial for ongoing support and enhancement.
### Your contribution
I am considering...
|
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I_kwDOIPDwls5tVJvF
| 8,664 |
ClickHouse VectorStore score_threshold not working.
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[] | null | 2 | 2023-08-03T04:44:00 | 2023-11-03T02:35:28 | 2023-11-03T02:35:21 |
NONE
| null |
### System Info
* python3.9
* langchain 0.0242
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
m3e: HuggingFaceEmbeddings = SentenceTransformerEmbeddings(model_name='moka-ai/m3e-base')
vectorStore: VectorStore = Clickhouse(embedding=m3e, config=settings)
retriever = vectorStore.as_retriever(search_type='similarity_score_threshold', search_kwargs={'score_threshold': 20.0})
documents = retriever.get_relevant_documents('音乐')
```
retriever invoke `_get_relevant_documents in vectorstores/base.py`, and `docs_and_similarities` has doc and score.
I found `score_threshold` is not used in `similarity_search_with_relevance_scores`.
Such as pgvector, it is implement `similarity_search_with_score`, so retriever invoke
chain is `similarity_search_with_relevance_scores(base.py)` -> `_similarity_search_with_relevance_scores(base.py)` -> `similarity_search_with_score(pgvector)`. So, score filter will be working in `similarity_search_with_relevance_scores(base.py)`.
But Clickhouse vectorstore return from `similarity_search_with_relevance_scores` and without score filter because clickhouse implement `similarity_search_with_relevance_scores` from `base.py`.
---
I have two suggestions for this issue.
1. use `score_threshold` in `similarity_search_with_relevance_scores`
```sql
SELECT document,
metadata, dist
FROM default.langchain
WHERE dist > {score_threshold}
ORDER BY L2Distance(embedding, [....])
AS dist ASC
LIMIT 4
```
2. rename function `similarity_search_with_relevance_scores` to `_similarity_search_with_relevance_scores`.
make invoke chain to `similarity_search_with_relevance_scores(base.py)` -> `_similarity_search_with_relevance_scores(clickhouse)`
got data that score less than score_threshold
### Expected behavior
score filtering normally in clickhouse
BTW, I can submit pr if necessary
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PR_kwDOIPDwls5XEPDA
| 8,663 |
Minor formatting and additional figure for summarization use case
|
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[] | null | 1 | 2023-08-03T04:10:38 | 2023-08-03T04:52:30 | 2023-08-03T04:52:29 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5tVBXI
| 8,662 |
Use FAISS {"distance_strategy":"MAX_INNER_PRODUCT"} Not work!
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[] | null | 2 | 2023-08-03T03:57:32 | 2023-08-03T06:53:06 | 2023-08-03T06:53:06 |
NONE
| null |
### System Info
python 3.10
langchain 0.0.249
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
loader = DirectoryLoader("content/zh/knowledge/", show_progress=True,loader_cls=TextLoader)
raw_documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=500,
chunk_overlap=0,
separators=['\n\n','\n','。','？','！','|']
)
# embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
embeddings = HuggingFaceEmbeddings(model_name="moka-ai/m3e-base")
count = int(len(raw_documents) / 100) +1
for index in range(count):
print('当前：%s' % index)
documents = text_splitter.split_documents(raw_documents[index*100:min((index+1)*100,len(raw_documents))-1])
vectorstore = FAISS.from_documents(documents=documents, embedding=embeddings,kwargs={"distance_strategy":"MAX_INNER_PRODUCT"})
# Save vectorstore
with open("vectorstore.pkl", "wb") as f:
pickle.dump(vectorstore, f)
### Expected behavior
Use FAISS kwargs={"distance_strategy":"MAX_INNER_PRODUCT"} Error! Not work!
<img width="1512" alt="image" src="https://github.com/langchain-ai/langchain/assets/4583537/fee0f9b9-49bc-4713-adb9-de8c9be76606">
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Amazon Textract as document loader
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[] | null | 9 | 2023-08-03T03:54:43 | 2023-08-07T10:30:44 | 2023-08-04T19:55:07 |
CONTRIBUTOR
| null |
Description: Adding support for [Amazon Textract](https://aws.amazon.com/textract/) as a PDF document loader
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I_kwDOIPDwls5tU6oW
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LlamaCppEmbeddings Error `type == GGML_TYPE_F16`
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[] | null | 3 | 2023-08-03T03:15:23 | 2023-11-09T16:13:07 | 2023-11-09T16:13:06 |
NONE
| null |
### System Info
```
llama_print_timings: load time = 127.16 ms
llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_print_timings: prompt eval time = 1753.93 ms / 237 tokens ( 7.40 ms per token, 135.13 tokens per second)
llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_print_timings: total time = 1779.79 ms
GGML_ASSERT: /tmp/pip-install-7c_dsjad/llama-cpp-python_04ef08a182034167b5fee4a62dd2cdc2/vendor/llama.cpp/ggml-cuda.cu:3572: src0->type == GGML_TYPE_F16
中止 (核心已傾印)
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [x] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
vectorstore = Chroma.from_documents(documents=all_splits, embedding=LlamaCppEmbeddings(model_path="/home/mefae1/llm/chinese-alpaca-2-7b/ggml-model-q4_0.bin",
n_gpu_layers=40,
n_batch=16,
n_ctx=2048
))
```
### Expected behavior
return vectorstore and not error
|
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| 1,834,165,151 |
PR_kwDOIPDwls5XEABu
| 8,659 |
Fix Async Retry Event Handling
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[] | null | 1 | 2023-08-03T02:24:43 | 2023-08-03T22:02:18 | 2023-08-03T22:02:17 |
COLLABORATOR
| null |
It fails currently because the event loop is already running.
The `retry` decorator alraedy infers an `AsyncRetrying` handler for coroutines (see [tenacity line](https://github.com/jd/tenacity/blob/aa6f8f0a2428de696b237d1a86bc131c1cdb707a/tenacity/__init__.py#L535)) However before_sleep always gets called synchronously (see [tenacity line](https://github.com/jd/tenacity/blob/aa6f8f0a2428de696b237d1a86bc131c1cdb707a/tenacity/__init__.py#L338)).
Instead, check for a running loop and use that it exists. Of course, it's running an async method synchronously which is not _nice_. Given how important LLMs are, it may make sense to have a task list or something but I'd want to chat with @nfcampos on where that would live.
This PR also fixes the unit tests to check the handler is called and to make sure the async test is run (it looks like it's just been being skipped). It would have failed prior to the proposed fixes but passes now.
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PR_kwDOIPDwls5XD8fF
| 8,658 |
Update spec.py
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[] | null | 1 | 2023-08-03T01:59:08 | 2023-08-08T14:33:21 | 2023-08-08T14:33:21 |
CONTRIBUTOR
| null |
- Description: The API doc passed to LLM only included the content of responses but did not include the content of requestBody, causing the agent to be unable to construct the correct request parameters based on the requestBody information. Add two lines of code fixed the bug,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: @hinthornw ,
- 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_kwDOIPDwls5XD7wf
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Minor typo fix
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[] | null | 1 | 2023-08-03T01:53:54 | 2023-08-03T06:20:26 | 2023-08-03T06:20:25 |
COLLABORATOR
| null |
Fix typo in doc-string.
|
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I_kwDOIPDwls5tUnaM
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DOC: <Running Agent as an Iterator>' prefix>
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[] | null | 1 | 2023-08-03T01:19:51 | 2023-11-09T16:11:04 | 2023-11-09T16:11:03 |
NONE
| null |
### Issue with current documentation:
```
# need to use GPT-4 here as GPT-3.5 does not understand, however hard you insist, that
# it should use the calculator to perform the final calculation
llm = ChatOpenAI(temperature=0, model="gpt-4")
```
This docs say that it need gpt -4，are there other models that can be used？For example, some open source large models LLM。H
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5XDkGf
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Use call directly for chain
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[] | null | 1 | 2023-08-02T23:55:04 | 2023-08-03T00:11:40 | 2023-08-03T00:11:39 |
COLLABORATOR
| null |
for run_on_dataset since the `run()` method requires a single output
|
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PR_kwDOIPDwls5XDgdO
| 8,654 |
docs: fix some grammar
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[] | null | 2 | 2023-08-02T23:25:07 | 2023-08-02T23:48:02 | 2023-08-02T23:48:02 |
CONTRIBUTOR
| null |
### Description
Fixes a grammar issue I noticed when reading through the documentation.
### Maintainers
@baskaryan
|
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PR_kwDOIPDwls5XDeQo
| 8,653 |
Use salt instead of datetime
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[] | null | 1 | 2023-08-02T23:08:16 | 2023-08-03T00:22:38 | 2023-08-03T00:15:50 |
COLLABORATOR
| null |
If you want to kick off two runs at the same time it'll cause errors. Use a uuid instead
|
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PR_kwDOIPDwls5XDeMT
| 8,652 |
fix enum error message
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[] | null | 1 | 2023-08-02T23:07:47 | 2023-08-03T00:11:28 | 2023-08-03T00:11:27 |
COLLABORATOR
| null |
could be a string so don't directly call value
|
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PR_kwDOIPDwls5XDYZJ
| 8,651 |
📖 docs: compact api reference
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[] | null | 6 | 2023-08-02T22:28:49 | 2023-08-24T16:06:16 | 2023-08-24T16:01:52 |
COLLABORATOR
| null |
Updated design of the "API Reference" text
Here is an example of the current format:
It changed to
`langchain.retrievers.ElasticSearchBM25Retriever` format. The same format as it is in the API Reference Toc.
It also resembles code:
`from langchain.retrievers import ElasticSearchBM25Retriever` (namespace THEN class_name)
Current format is
`ElasticSearchBM25Retriever from langchain.retrievers` (class_name THEN namespace)
This change is in line with other formats and improves readability.
@baskaryan
|
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PR_kwDOIPDwls5XDVu7
| 8,650 |
Update to use non-pydantic run schema
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[] | null | 2 | 2023-08-02T22:12:35 | 2023-09-20T17:35:49 | null |
COLLABORATOR
| null |
Slowly trying to remove inhibitions on letting users upgrade to pydantic v2.
Depends on https://github.com/langchain-ai/langsmith-sdk/pull/127 or something similar being landed and an actual 0.1.0 to be cut before this can land / pass CI
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I_kwDOIPDwls5tUCLM
| 8,649 |
DOC: Agent Usecase Links are broken
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[] | null | 1 | 2023-08-02T22:11:00 | 2023-11-08T16:06:39 | 2023-11-08T16:06:38 |
NONE
| null |
### Issue with current documentation:
The documentation links seems to be broken, the Agents Use-cases links are no longer working and giving "Page Not Found".
Example Links - https://python.langchain.com/docs/use_cases/autonomous_agents/baby_agi_with_agent.html
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5tUAhf
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Integrate OPENAI_FUNCTIONS in Agent
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[] | null | 1 | 2023-08-02T22:02:54 | 2023-11-08T16:06:44 | 2023-11-08T16:06:43 |
NONE
| null |
### Issue you'd like to raise.
Following is the link of langchain documents. In this I need to integrate OPENAI_FUNCTIONS
https://python.langchain.com/docs/modules/agents/how_to/custom_llm_chat_agent
### Suggestion:
_No response_
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Newspaper
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CONTRIBUTOR
| null |
- Description: Added newspaper3k based news article loader. Provide a list of urls.
- Issue: N/A
- Dependencies: newspaper3k,
- Tag maintainer: @rlancemartin , @eyurtsev
- Twitter handle: @ruze
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PR_kwDOIPDwls5XDFjI
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Add templates
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[] | null | 4 | 2023-08-02T20:57:37 | 2023-08-04T20:11:00 | 2023-08-04T20:10:59 |
COLLABORATOR
| null |
Add documentation templates
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Amazon Textract as document loader added
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CONTRIBUTOR
| null |
Description: Adding support for [Amazon Textract](https://aws.amazon.com/textract/) as a PDF document loader
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. -> checked and worked fine
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I_kwDOIPDwls5tTE3Y
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Deterministic fake embedding model
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[] | null | 2 | 2023-08-02T18:36:33 | 2023-08-04T17:51:01 | 2023-08-04T17:51:01 |
CONTRIBUTOR
| null |
### Feature request
An implementation of a `FakeEmbeddingModel` that generates identical vectors given identical input texts.
### Motivation
Currently langchain has a `FakeEmbedding` model that generates a vector of random numbers, that is irrelevant to the content that needs to be embedded.
This model is pretty useful in e.g., unit tests, because it doesn't need to load any actual models, or connect to the internet.
However, there is one realistic feature it misses, which makes it "fake" compared to a realistic embedding model -- given input texts are the same, the generated embedding vectors should be the same as well.
This will make unit tests that involve using a `FakeEmbeddingModel` more realistic. .E.g., ou can test if the similarity search makes sense -- searching by the exact identical text from your vector store should give me an exact match, because the embeddings are identical, which gives a distance of 0.
### Your contribution
I can submit a PR for it.
We can use the hash of the text as a seed when generating random numbers.
In this case, if input texts are identical, we should get exactly the same embedding vectors.
|
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| 8,642 |
`MLflow` examples
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[] | null | 1 | 2023-08-02T18:33:24 | 2023-08-02T21:23:57 | 2023-08-02T20:30:28 |
COLLABORATOR
| null |
Updated `MLflow` examples with links to the examples from MLflow
@baskaryan
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Enterprise support form wording
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Harrison/conversational retrieval agent
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| 8,638 |
GraphQL Execution Tool should support URL/headers as input at runtime
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[] | null | 2 | 2023-08-02T17:02:44 | 2023-11-08T16:06:50 | 2023-11-08T16:06:49 |
NONE
| null |
### Feature request
Current GraphQL Tool is very static https://python.langchain.com/docs/modules/agents/tools/integrations/graphql, one has to set up graphql url, headers etc at the time of configuring the tool.
This tool should follow [Request*Tool](https://github.com/hwchase17/langchain/blob/2b3da522eb866cb79dfdc9116a7c466b866cb3d0/langchain/tools/requests/tool.py#L53), where individual request information is sent as input to this tool. This way, GraphQL tool will become a generic and could be used to query different endpoints, or with different headers
This issue was wrongly created in the l[angchainjs repo](https://github.com/hwchase17/langchainjs/issues/1713) instead of the python langchain repo (this one).
### Motivation
We have a use case where the URL and headers would change between invocation of this tool. And these could be very dynamic, every user might have a different "Authorization" header. So we want to register the tool once to the agent, but override these values at the time of invocation.
### Your contribution
Yes, my team is willing to contribute.
Here is the pull request https://github.com/langchain-ai/langchain/pull/8616
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| 8,637 |
Catch and Filter OpenAI "As an AI model" responses
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[] | null | 5 | 2023-08-02T16:50:53 | 2023-11-06T01:05:50 | null |
NONE
| null |
### Feature request
When connecting to ChatGPT API, it is possible to get responses by the chatbot as "As an AI model...", which ruins the chatbot experience on your application.
A way to filter this and generate a default response might be better.
### Motivation
For applications using OpenAI API, this would enable filtering those requests.
### Your contribution
Not really
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Elasticsearch Store Improvements
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[] | null | 14 | 2023-08-02T16:13:04 | 2023-08-15T08:47:49 | 2023-08-15T06:42:35 |
CONTRIBUTOR
| null |
Todo:
- [x] Connection options (cloud, localhost url, es_connection) support
- [x] Logging support
- [x] Customisable field support
- [x] Distance Similarity support
- [x] Metadata support
- [x] Metadata Filter support
- [x] Retrieval Strategies
- [x] Approx
- [x] Approx with Hybrid
- [x] Exact
- [x] Custom
- [x] ELSER (excluding hybrid as we are working on RRF support)
- [x] integration tests
- [x] Documentation
👋 this is a contribution to improve Elasticsearch integration with Langchain. Its based loosely on the changes that are in master but with some notable changes:
## Package name & design improvements
The import name is now `ElasticsearchStore`, to aid discoverability of the VectorStore.
```py
## Before
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch, ElasticKnnSearch
## Now
from langchain.vectorstores.elasticsearch import ElasticsearchStore
```
## Retrieval Strategy support
Before we had a number of classes, depending on the strategy you wanted. `ElasticKnnSearch` for approx, `ElasticVectorSearch` for exact / brute force.
With `ElasticsearchStore` we have retrieval strategies:
### Approx Example
Default strategy for the vast majority of developers who use Elasticsearch will be inferring the embeddings from outside of Elasticsearch. Uses KNN functionality of _search.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index"
)
output = docsearch.similarity_search("foo", k=1)
```
### Approx, with hybrid
Developers who want to search, using both the embedding and the text bm25 match. Its simple to enable.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True)
)
output = docsearch.similarity_search("foo", k=1)
```
### Approx, with `query_model_id`
Developers who want to infer within Elasticsearch, using the model loaded in the ml node.
This relies on the developer to setup the pipeline and index if they wish to embed the text in Elasticsearch. Example of this in the test.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ApproxRetrievalStrategy(
query_model_id="sentence-transformers__all-minilm-l6-v2"
),
)
output = docsearch.similarity_search("foo", k=1)
```
### I want to provide my own custom Elasticsearch Query
You might want to have more control over the query, to perform multi-phase retrieval such as LTR, linearly boosting on document parameters like recently updated or geo-distance. You can do this with `custom_query_fn`
```py
def my_custom_query(query_body: dict, query: str) -> dict:
return {"query": {"match": {"text": {"query": "bar"}}}}
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts, FakeEmbeddings(), **elasticsearch_connection, index_name=index_name
)
docsearch.similarity_search("foo", k=1, custom_query=my_custom_query)
```
### Exact Example
Developers who have a small dataset in Elasticsearch, dont want the cost of indexing the dims vs tradeoff on cost at query time. Uses script_score.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.ExactRetrievalStrategy(),
)
output = docsearch.similarity_search("foo", k=1)
```
### ELSER Example
Elastic provides its own sparse vector model called ELSER. With these changes, its really easy to use. The vector store creates a pipeline and index thats setup for ELSER. All the developer needs to do is configure, ingest and query via langchain tooling.
```py
texts = ["foo", "bar", "baz"]
docsearch = ElasticsearchStore.from_texts(
texts,
FakeEmbeddings(),
es_url="http://localhost:9200",
index_name="sample-index",
strategy=ElasticsearchStore.SparseVectorStrategy(),
)
output = docsearch.similarity_search("foo", k=1)
```
## Architecture
In future, we can introduce new strategies and allow us to not break bwc as we evolve the index / query strategy.
## Credit
On release, could you credit @elastic and @phoey1 please? Thank you!
|
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| 1,833,455,388 |
I_kwDOIPDwls5tSE8c
| 8,635 |
Support parallel upserts in Pinecone
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[] | null | 1 | 2023-08-02T15:54:23 | 2023-09-05T14:59:11 | 2023-09-05T14:59:11 |
NONE
| null |
### Feature request
It would be great if there was an explicit way to support parallel upserts in the `Pinecone` vectorstore wrapper. This could either be done by altering the existing `add_texts` method or adding a new "async" equivalent.
### Motivation
The native Pinecone client supports [parallel upsert](https://docs.pinecone.io/docs/insert-data#sending-upserts-in-parallel) operations which can improve throughput. This is technically supported with the current `langchain` vectorstore wrapper using the following combination of arguments `index.add_texts(texts, async_req=True, batch_size=None)`.
However, because `add_texts` [returns](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/pinecone.py#L102-L105) the list of `ids` instead of the response to `self._index.upsert`, we cannot call `get` to suspend the program until all of the responses are available (see code snippet from the Pinecone docs below).
```
# Upsert data with 100 vectors per upsert request asynchronously
# - Create pinecone.Index with pool_threads=30 (limits to 30 simultaneous requests)
# - Pass async_req=True to index.upsert()
with pinecone.Index('example-index', pool_threads=30) as index:
# Send requests in parallel
async_results = [
index.upsert(vectors=ids_vectors_chunk, async_req=True)
for ids_vectors_chunk in chunks(example_data_generator, batch_size=100)
]
# Wait for and retrieve responses (this raises in case of error)
[async_result.get() for async_result in async_results]
```
### Your contribution
I can try to put a PR up for this. Although I wonder if there was a deliberate decision to not return the raw response for [`upsert`](https://github.com/pinecone-io/pinecone-python-client/blob/main/pinecone/index.py#L73-L78) in order to have a cleaner abstraction.
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PR_kwDOIPDwls5XBVkz
| 8,634 |
Refactor Qdrant integration
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[] | null | 2 | 2023-08-02T15:15:11 | 2023-08-02T17:30:19 | 2023-08-02T17:30:18 |
CONTRIBUTOR
| null |
This small PR introduces new parameters into Qdrant (`on_disk`), fixes some tests and changes the error message to be more clear.
Tagging: @baskaryan, @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5XBJ0t
| 8,633 |
Eden AI (LLM + embeddings) integration
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[] | null | 5 | 2023-08-02T14:44:56 | 2023-08-04T07:07:08 | 2023-08-03T17:25:19 |
CONTRIBUTOR
| null |
# Eden AI - Integration
This PR adds [EdenAI](https://edenai.co) as an LLM and Embeddings provider.
Eden AI is a Meta API regrouping all the best AI APIs in the market (Amazon, Open AI, Cohere, Google, Anthropic, DeepL, AssemblyAI ...etc) into one. Users can access all the AI tasks you need with a single account giving them total freedom switch between providers and combine them.
This allows langchain users to :
💪 Increase Accuracy & Reduce Cost by choosing the right AI API for each AI task
☮️ Gain peace of mind with centralized usage restrictions & cost monitoring
🌐 Continually explore emerging AI capabilities in the market
Here is an example of how it's called:
```python
from langchain.llms import EdenAI
llm = EdenAI( edenai_api_key="...",
provider="openai",
params={"temperature" : 0.2,"max_tokens" : 250})
```
It can also be used for text2image :
```python
imagemodel = EdenAI( feature="image" , provider= "stabilityai",
params={ "resolution" : "512x512" })
```
A detailed notebook was added.
No dependencies are added as we call a rest API.
**Upcoming** : This PR starts by adding the LLM and Embeddings capabilities and will be followed by a utilities PR to give agents new capabilities like : explicit content detection (image, texts and videos), speech to text and text to speech, pdf parsing (invoice, ID, resume...etc ) and a lot of other tasks (+50) from top AI APIs providers.
Ping: @hwchase17, @baskaryan
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PR_kwDOIPDwls5XBIAD
| 8,632 |
bump 250
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[] | null | 0 | 2023-08-02T14:40:13 | 2023-08-02T14:47:38 | 2023-08-02T14:47:37 |
COLLABORATOR
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I_kwDOIPDwls5tRc3I
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Pubmed needs to be called with "pupo
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[] | null | 2 | 2023-08-02T14:22:29 | 2023-11-08T16:06:55 | 2023-11-08T16:06:54 |
NONE
| null |
### System Info
OS: Windows
Name: langchain
Version: 0.0.249
Python 3.11.2
### Who can help?
@hw
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When loading the pubmed Tool it has to be called with "pupmed". Though this was possibly a typo since pupmed isn't mentioned in the documents.
### Expected behavior
Expect to call the pubmed tool with "pubmed" and not "pupmed" or documentation should be more clear.
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PR_kwDOIPDwls5XALg2
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add missing RemoteLangChainRetriever _get_relevant_documents test
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[] | null | 1 | 2023-08-02T12:01:29 | 2023-08-03T07:43:34 | 2023-08-03T00:20:41 |
CONTRIBUTOR
| null |
# What
- Add missing RemoteLangChainRetriever _get_relevant_documents test
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add missing RemoteLangChainRetriever _get_relevant_documents test
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- 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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| 1,833,027,175 |
PR_kwDOIPDwls5XAHTc
| 8,627 |
Swap in Sympy instead of numexpr for more expressive LLMMath tool
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[] | null | 5 | 2023-08-02T11:48:21 | 2023-11-20T20:47:04 | 2023-11-10T22:41:11 |
CONTRIBUTOR
| null |
- Description: Swapped in `sympy` instead of `numexpr` in the LLMMath Chain
- Issue: Relevant to #8363
- Dependencies: None
- Tag maintainer: @hinthornw
Note that we already use `sympy` in the symbolic math tool, so this actually removes a dependency!
`numexpr` is actually quite fragile to input, whereas `sympify` is really quite hard to break. Take the iconic Langchain tool example mixing information retrieval and numerical calculation: _"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"_ (from this [tutorial page](https://python.langchain.com/docs/modules/agents/how_to/intermediate_steps)).
If you make the maths more exotic ("What is the first prime number larger than her age?", "What is the factorial of her age?", etc.) `numexpr` will raise an exception. `sympy` handles these with flying colours.
I used the chain from that example, with the prompt:
```
"In the sky, I am the number of sisters who dance, Bound by myth, in a celestial trance. On Earth, I am the continents that span, Holding diverse life, since time began.
What number am I?
Now multiply that number by the age of Zoe Saldana's partner, and raise it to the 2.43rd power.
Take the fourth digit of the answer, then take the factorial of that, and call it X.
What's the smallest prime number greater than X?
"""
```
and with `sympy`, my chain passed with flying colours :)
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I_kwDOIPDwls5tQWVL
| 8,625 |
How to connect a Chatbot that has its own data but has also access to internet for search?
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[] | null | 8 | 2023-08-02T11:32:47 | 2023-11-11T16:06:12 | 2023-11-11T16:06:11 |
NONE
| null |
### Issue you'd like to raise.
Hi!
How to make a Chatbot that uses its own data access to the internet to get more info (like new updated)? I've tried and searched everywhere but can't make it work.
Here the code:
`
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import (
UnstructuredWordDocumentLoader,
TextLoader,
UnstructuredPowerPointLoader,
)
from langchain.tools import Tool
from langchain.utilities import GoogleSearchAPIWrapper
from langchain.chat_models import ChatOpenAI
import os
import openai
import sys
from dotenv import load_dotenv, find_dotenv
sys.path.append('../..')
_ = load_dotenv(find_dotenv()) # read local .env file
google_api_key = os.environ.get("GOOGLE_API_KEY")
google_cse_id = os.environ.get("GOOGLE_CSE_ID")
openai.api_key = os.environ['OPENAI_API_KEY']
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.langchain.plus"
os.environ["LANGCHAIN_API_KEY"] = os.environ['LANGCHAIN_API_KEY']
os.environ["GOOGLE_API_KEY"] = google_api_key
os.environ["GOOGLE_CSE_ID"] = google_cse_id
folder_path_docx = "DB\\DB VARIADO\\DOCS"
folder_path_txt = " DB\\BLOG-POSTS"
folder_path_pptx_1 = "DB\\PPT JUNIO"
folder_path_pptx_2 = "DB\\DB VARIADO\\PPTX"
loaded_content = []
for file in os.listdir(folder_path_docx):
if file.endswith(".docx"):
file_path = os.path.join(folder_path_docx, file)
loader = UnstructuredWordDocumentLoader(file_path)
docx = loader.load()
loaded_content.extend(docx)
for file in os.listdir(folder_path_txt):
if file.endswith(".txt"):
file_path = os.path.join(folder_path_txt, file)
loader = TextLoader(file_path, encoding='utf-8')
text = loader.load()
loaded_content.extend(text)
for file in os.listdir(folder_path_pptx_1):
if file.endswith(".pptx"):
file_path = os.path.join(folder_path_pptx_1, file)
loader = UnstructuredPowerPointLoader(file_path)
slides_1 = loader.load()
loaded_content.extend(slides_1)
for file in os.listdir(folder_path_pptx_2):
if file.endswith(".pptx"):
file_path = os.path.join(folder_path_pptx_2, file)
loader = UnstructuredPowerPointLoader(file_path)
slides_2 = loader.load()
loaded_content.extend(slides_2)
embedding = OpenAIEmbeddings()
embeddings_content = []
for one_loaded_content in loaded_content:
embedding_content = embedding.embed_query(one_loaded_content.page_content)
embeddings_content.append(embedding_content)
db = DocArrayInMemorySearch.from_documents(loaded_content, embedding)
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
search = GoogleSearchAPIWrapper()
def custom_search(query):
max_results = 3
internet_results = search.run(query)[:max_results]
return internet_results
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(model_name="gpt-4", temperature=0),
chain_type="map_reduce",
retriever=retriever,
return_source_documents=True,
return_generated_question=True,
)
history = []
while True:
query = input("Hola, soy Chatbot. ¿Qué te gustaría saber? ")
internet_results = custom_search(query)
combined_results = loaded_content + [internet_results]
response = chain(
{"question": query, "chat_history": history, "documents": combined_results})
print(response["answer"])
history.append(("system", query))
history.append(("assistant", response["answer"]))
`
This is the error message I get: "The document does not provide information on... ". So it seems it doesn't access to the internet or something else (?)
Really appreciate your suggestion or your help!
### Suggestion:
_No response_
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PR_kwDOIPDwls5W_0ca
| 8,624 |
Add upsert support for pgvector
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[] | null | 3 | 2023-08-02T10:51:00 | 2023-11-17T03:07:15 | null |
NONE
| null |
## Description
Currently, there is no support for updates or upserts to PGVector through langchain.
This PR adds that support by leveraging `on_conflict_do_upsert`
This fixes https://github.com/langchain-ai/langchain/issues/6866
Maintainers: @rlancemartin, @eyurtsev
Twitter handle: [@lokeshdevnani](https://twitter.com/lokeshdevnani)
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I_kwDOIPDwls5tP_Yv
| 8,623 |
How to create a custom retriever
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[] | null | 11 | 2023-08-02T10:30:14 | 2023-10-06T09:47:10 | 2023-08-23T09:51:02 |
NONE
| null |
### Feature request
When I use RetrievalQA, I need to add and reorder the content retrieved by retriever
```
qa = RetrievalQA.from_chain_type(llm=chat,
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={"k": 2}),
chain_type_kwargs={"prompt": PROMPT,"memory":memory })
```
I want the retrieved content from retriever to be reordered by "source".
The following code is used by me to manipulate the retrieved content to achieve my needs.
```
retriever = docsearch.as_retriever(search_kwargs={"k": 6})
search_result = retriever.get_relevant_documents(quey)
def sort_paragraphs(paragraphs):
sorted_paragraphs = sorted(paragraphs, key=lambda x: x.metadata["source"])
return sorted_paragraphs
paragraphs = sort_paragraphs(search_result)
sorted_paragraphs = ""
for i in paragraphs:
sorted_paragraphs = sorted_paragraphs + i.page_content + "\n"
```
How can I define an retriever that I can use in my chain to organize the search content as I wish
### Motivation
Some custom retrieval logic is required to seek optimal performance.
### Your contribution
I would like to have tutorials related to custom retriever documentation
@hwchase17
https://twitter.com/hwchase17/status/1646272240202432512
I think langchain needs to be a custom retriever
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PR_kwDOIPDwls5W_p5T
| 8,622 |
Fix Issue #6650: Enable Azure Active Directory token-based auth access for AzureChatOpenAI
|
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[] | null | 3 | 2023-08-02T10:21:13 | 2023-08-04T03:21:41 | 2023-08-04T03:21:41 |
CONTRIBUTOR
| null |
When using AzureChatOpenAI the openai_api_type defaults to "azure". The utils' get_from_dict_or_env() function triggered by the root validator does not look for user provided values from environment variables OPENAI_API_TYPE, so other values like "azure_ad" are replaced with "azure". This does not allow the use of token-based auth.
By removing the "default" value, this allows environment variables to be pulled at runtime for the openai_api_type and thus enables the other api_types which are expected to work.
This fixes #6650
|
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PR_kwDOIPDwls5W_f6A
| 8,621 |
Feature/fix azureopenai model mappings
|
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[] | null | 4 | 2023-08-02T09:53:53 | 2023-08-09T18:39:00 | 2023-08-09T17:56:15 |
CONTRIBUTOR
| null |
This pull request aims to ensure that the `OpenAICallbackHandler` can properly calculate the total cost for Azure OpenAI chat models. The following changes have resolved this issue:
- The `model_name` has been added to the ChatResult llm_output. Without this, the default values of `gpt-35-turbo` were applied. This was causing the total cost for Azure OpenAI's GPT-4 to be significantly inaccurate.
- A new parameter `model_version` has been added to `AzureChatOpenAI`. Azure does not include the model version in the response. With the addition of `model_name`, this is not a significant issue for GPT-4 models, but it's an issue for GPT-3.5-Turbo. Version 0301 (default) of GPT-3.5-Turbo on Azure has a flat rate of 0.002 per 1k tokens for both prompt and completion. However, version 0613 introduced a split in pricing for prompt and completion tokens.
- The `OpenAICallbackHandler` implementation has been updated with the proper model names, versions, and cost per 1k tokens.
Unit tests have been added to ensure the functionality works as expected; the Azure ChatOpenAI notebook has been updated with examples.
Maintainers: @hwchase17, @baskaryan
Twitter handle: @jjczopek
|
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I_kwDOIPDwls5tPC3R
| 8,620 |
MarkdownHeaderTextSplitter is not a TextSplitter
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[] | null | 6 | 2023-08-02T08:06:38 | 2023-11-10T07:39:18 | 2023-11-10T07:39:09 |
CONTRIBUTOR
| null |
### System Info
The class `MarkdownHeaderTextSplitter` is not a `TextSplitter`, and must implements all the corresponding methods.
```
class MarkdownHeaderTextSplitter:
...
```
@hwchase17 @eyurtsev
### 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
headers_to_split_on = [
("#", "Header 1"),
("##", "Header 2"),
("###", "Header 3"),
]
splitter = MarkdownHeaderTextSplitter(
headers_to_split_on=headers_to_split_on
)
splitter.split_documents(docs)
### Expected behavior
Accept the call
|
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| 1,832,655,316 |
I_kwDOIPDwls5tPBnU
| 8,619 |
Langchain Support for Onnx Llama
|
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[] | null | 5 | 2023-08-02T08:03:06 | 2023-11-25T17:35:20 | null |
NONE
| null |
### Feature request
Recently Microsoft have announced their [first iteration of running Llama using Onnx format](https://github.com/microsoft/Llama-2-Onnx/tree/main). Hence it will be awesome if LangChain comes up with an early support for Onnx runtime models.
### Motivation
There are two reasons for this.
1. Onnx has always been the standard for running inference in CPU / GPU (Onnx GPU), so this idea of providing LLMs supported for Onnx runtime format will move forward fast
2. Current implementations of running the same is an overhead, LangChain can provide the abstraction easily.
### Your contribution
I can try starting out to experiment with this of whether we can implement or not by using the existing LLM interface. However the bottleneck here becomes the .onnx format weights which are to be requested to Microsoft. I filled out the application, waiting for approval. Let me know if we can work on this issue.
|
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I_kwDOIPDwls5tOrfD
| 8,618 |
AgentType.OPENAI_FUNCTIONS did not work
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[] | null | 2 | 2023-08-02T07:02:13 | 2023-11-08T16:07:05 | 2023-11-08T16:07:04 |
NONE
| null |
### System Info
langchain 0.0.249
Python 3.11.2
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I follow the egs of openai_functions_agent at
https://python.langchain.com/docs/modules/agents/
https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent
https://python.langchain.com/docs/modules/agents/how_to/custom-functions-with-openai-functions-agent
```
from langchain.chat_models import ChatOpenAI
from langchain.schema import SystemMessage
from langchain.agents import OpenAIFunctionsAgent
from langchain.agents import tool
from langchain.agents import AgentExecutor
llm = ChatOpenAI(temperature=0)
@tool
def get_word_length(word: str) -> int:
"""Returns the length of a word."""
import pdb # never go in here
pdb.set_trace()
return len(word)
tools = [get_word_length]
system_message = SystemMessage(content="You are very powerful assistant, but bad at calculating lengths of words")
prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.run("how many letters in the word educa?")
```
But the openai_functions_agent do not invoke the tool as expected。
```
> Entering new AgentExecutor chain...
[{'role': 'system', 'content': 'You are very powerful assistant, but bad at calculating lengths of words'}, {'role': 'user', 'content': 'how many letters in the word educa?'}]
There are 5 letters in the word "educa".
> Finished chain.
```
I tried using other tools but they were not used . It seems that OPENAI_FUNCTIONS has bugs。
### Expected behavior
AgentType.OPENAI_FUNCTIONS works as docs show
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PR_kwDOIPDwls5W-cC2
| 8,617 |
cast to string when measuring token length
|
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[] | null | 1 | 2023-08-02T06:38:13 | 2023-08-02T07:13:00 | 2023-08-02T07:12:59 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5W-ZV_
| 8,616 |
Enhance GraphQL Tool to accept Endpoint, Headers and Query Variables at the time of Query Execution
|
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NONE
| null |
- Description: The current GraphQL tool requires graphql endpoint and request headers at the time of the tool instantiation which makes the tool less flexible as it could be used only to run queries against a single endpoint and using the same headers. This pull-request contains changes to allow using a different graphql endpoint and request headers at the time of the query execution making the tool dynamic. Secondly, this pull-request further enhances the GraphQL tool by accepting query variables besides the GraphQL query.
- Issue: https://github.com/hwchase17/langchainjs/issues/1713
- Dependencies: None
- Tag maintainer: @hinthornw
- Twitter handle: None
|
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PR_kwDOIPDwls5W-Myy
| 8,614 |
fix: calling the openai functions agent with an empty tool list crashes
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[] | null | 5 | 2023-08-02T05:32:04 | 2023-11-13T06:17:35 | 2023-11-13T06:17:34 |
NONE
| null |
Fixes Issue https://github.com/langchain-ai/langchain/issues/8080
No new dependencies have been added.
@baskaryan
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I_kwDOIPDwls5tOPc6
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Invalid model when use chain
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[] | null | 0 | 2023-08-02T05:17:12 | 2023-08-02T07:13:12 | 2023-08-02T07:13:12 |
NONE
| null |
### System Info
langchain: 0.0.246
python: 3.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I want to get a summary from chat history between ai-bot and user.
- ChatOpenAI model gpt-3.5-turbo
- LLMChain
But got an Error "openai.error.InvalidRequestError: This is a chat model and not supported in the v1/completions endpoint. Did you mean to use v1/chat/completions?"
My Code
`
memory = get_memory_from_chat_prompt(messages)
prompt = PromptTemplate(
input_variables=SUMMARIZE_INPUT_VARIABLES,
template=SUMMARIZE_CHAT_TEMPLATE
)
llm = ChatOpenAI(temperature=.1, model_name='gpt-3.5-turbo', max_tokens=1000)
human_input="""Please provide a summary. You can include the key points or main findings in a concise manner."""
answer_chain = LLMChain(llm=llm, prompt=prompt, memory=memory)
result = answer_chain.predict(human_input=human_input)
`
Error:
`
File "/Users/congle/working/bamboo/bot/app/package/aianswer.py", line 78, in get_summarize_from_messages
result = answer_chain.predict(human_input=human_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/chains/llm.py", line 252, in predict
return self(kwargs, callbacks=callbacks)[self.output_key]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/chains/base.py", line 260, in __call__
final_outputs: Dict[str, Any] = self.prep_outputs(
^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/chains/base.py", line 354, in prep_outputs
self.memory.save_context(inputs, outputs)
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/memory/summary_buffer.py", line 60, in save_context
self.prune()
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/memory/summary_buffer.py", line 71, in prune
self.moving_summary_buffer = self.predict_new_summary(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/memory/summary.py", line 37, in predict_new_summary
return chain.predict(summary=existing_summary, new_lines=new_lines)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/chains/llm.py", line 252, in predict
return self(kwargs, callbacks=callbacks)[self.output_key]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/chains/base.py", line 258, in __call__
raise e
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/chains/base.py", line 252, in __call__
self._call(inputs, run_manager=run_manager)
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/chains/llm.py", line 92, in _call
response = self.generate([inputs], run_manager=run_manager)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/chains/llm.py", line 102, in generate
return self.llm.generate_prompt(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/llms/base.py", line 451, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/llms/base.py", line 582, in generate
output = self._generate_helper(
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/llms/base.py", line 488, in _generate_helper
raise e
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/llms/base.py", line 475, in _generate_helper
self._generate(
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/llms/openai.py", line 400, in _generate
response = completion_with_retry(
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/llms/openai.py", line 116, in completion_with_retry
return _completion_with_retry(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
^^^^^^^^^^^^
File "/opt/homebrew/Cellar/[email protected]/3.11.4_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/[email protected]/3.11.4_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/langchain/llms/openai.py", line 114, in _completion_with_retry
return llm.client.create(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/openai/api_resources/completion.py", line 25, in create
return super().create(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/openai/api_requestor.py", line 298, in request
resp, got_stream = self._interpret_response(result, stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/openai/api_requestor.py", line 700, in _interpret_response
self._interpret_response_line(
File "/Users/congle/.local/share/virtualenvs/bot-f4x5-mrU/lib/python3.11/site-packages/openai/api_requestor.py", line 763, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: This is a chat model and not supported in the v1/completions endpoint. Did you mean to use v1/chat/completions?
`
### Expected behavior
I expect to get summary from chat history by model gpt-3.5. Thanks for your help!
|
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| 1,832,435,503 |
PR_kwDOIPDwls5W-HJT
| 8,612 |
Redis metadata filtering and specification, index customization
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[] | null | 2 | 2023-08-02T04:58:55 | 2023-11-13T17:39:13 | 2023-08-26T00:22:51 |
CONTRIBUTOR
| null |
### Description
The previous Redis implementation did not allow for the user to specify the index configuration (i.e. changing the underlying algorithm) or add additional metadata to use for querying (i.e. hybrid or "filtered" search).
This PR introduces the ability to specify custom index attributes and metadata attributes as well as use that metadata in filtered queries. Overall, more structure was introduced to the Redis implementation that should allow for easier maintainability moving forward.
### Example data
Suppose we have the following sample data
```python
metadata = [
{
"user": "john",
"age": 18,
"job": "engineer",
"credit_score": "high",
},
{
"user": "derrick",
"age": 14,
"job": "doctor",
"credit_score": "low",
},
{
"user": "nancy",
"age": 94,
"job": "doctor",
"credit_score": "high",
},
{
"user": "tyler",
"age": 100,
"job": "engineer",
"credit_score": "high",
},
{
"user": "tim",
"age": 12,
"job": "dermatologist",
"credit_score": "high",
},
{
"user": "taimur",
"age": 15,
"job": "CEO",
"credit_score": "low",
},
{
"user": "joe",
"age": 35,
"job": "dentist",
"credit_score": "medium",
},
]
texts = ["foo", "foo", "foo", "foo", "bar", "bar", "bar"]
```
# New Features
The following features are now available with the Redis integration into Langchain
## Index schema generation
The schema for the index will now be automatically generated if not specified by the user. For example, the data above has the multiple metadata categories. The the following example
```python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.redis import Redis
embeddings = OpenAIEmbeddings()
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users"
)
```
Loading the data in through this and the other ``from_documents`` and ``from_texts`` methods will now generate index schema in Redis like the following.
view index schema with the ``redisvl`` tool. [link](redisvl.com)
```bash
$ rvl index info -i users
```
Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |
|--------------|----------------|---------------|-----------------|------------|
| users | HASH | ['doc:users'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |
|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |
### Custom Metadata specification
The metadata schema generation has the following rules
1. All text fields are indexed as text fields.
2. All numeric fields are index as numeric fields.
If you would like to have a text field as a tag field, users can specify overrides like the following for the example data
```python
# this can also be a path to a yaml file
index_schema = {
"text": [{"name": "user"}, {"name": "job"}],
"tag": [{"name": "credit_score"}],
"numeric": [{"name": "age"}],
}
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users"
)
```
This will change the index specification to
Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |
|--------------|----------------|----------------|-----------------|------------|
| users2 | HASH | ['doc:users2'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |
|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TAG | SEPARATOR | , |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |
and throw a warning to the user (log output) that the generated schema does not match the specified schema.
```text
index_schema does not match generated schema from metadata.
index_schema: {'text': [{'name': 'user'}, {'name': 'job'}], 'tag': [{'name': 'credit_score'}], 'numeric': [{'name': 'age'}]}
generated_schema: {'text': [{'name': 'user'}, {'name': 'job'}, {'name': 'credit_score'}], 'numeric': [{'name': 'age'}]}
```
As long as this is on purpose, this is fine.
The schema can be defined as a yaml file or a dictionary
```yaml
text:
- name: user
- name: job
tag:
- name: credit_score
numeric:
- name: age
```
and you pass in a path like
```python
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users3",
index_schema=Path("sample1.yml").resolve()
)
```
Which will create the same schema as defined in the dictionary example
Index Information:
| Index Name | Storage Type | Prefixes | Index Options | Indexing |
|--------------|----------------|----------------|-----------------|------------|
| users3 | HASH | ['doc:users3'] | [] | 0 |
Index Fields:
| Name | Attribute | Type | Field Option | Option Value |
|----------------|----------------|---------|----------------|----------------|
| user | user | TEXT | WEIGHT | 1 |
| job | job | TEXT | WEIGHT | 1 |
| content | content | TEXT | WEIGHT | 1 |
| credit_score | credit_score | TAG | SEPARATOR | , |
| age | age | NUMERIC | | |
| content_vector | content_vector | VECTOR | | |
### Custom Vector Indexing Schema
Users with large use cases may want to change how they formulate the vector index created by Langchain
To utilize all the features of Redis for vector database use cases like this, you can now do the following to pass in index attribute modifiers like changing the indexing algorithm to HNSW.
```python
vector_schema = {
"algorithm": "HNSW"
}
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users3",
vector_schema=vector_schema
)
```
A more complex example may look like
```python
vector_schema = {
"algorithm": "HNSW",
"ef_construction": 200,
"ef_runtime": 20
}
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users3",
vector_schema=vector_schema
)
```
All names correspond to the arguments you would set if using Redis-py or RedisVL. (put in doc link later)
### Better Querying
Both vector queries and Range (limit) queries are now available and metadata is returned by default. The outputs are shown.
```python
>>> query = "foo"
>>> results = rds.similarity_search(query, k=1)
>>> print(results)
[Document(page_content='foo', metadata={'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '14', 'id': 'doc:users:657a47d7db8b447e88598b83da879b9d', 'score': '7.15255737305e-07'})]
>>> results = rds.similarity_search_with_score(query, k=1, return_metadata=False)
>>> print(results) # no metadata, but with scores
[(Document(page_content='foo', metadata={}), 7.15255737305e-07)]
>>> results = rds.similarity_search_limit_score(query, k=6, score_threshold=0.0001)
>>> print(len(results)) # range query (only above threshold even if k is higher)
4
```
### Custom metadata filtering
A big advantage of Redis in this space is being able to do filtering on data stored alongside the vector itself. With the example above, the following is now possible in langchain. The equivalence operators are overridden to describe a new expression language that mimic that of [redisvl](redisvl.com). This allows for arbitrarily long sequences of filters that resemble SQL commands that can be used directly with vector queries and range queries.
There are two interfaces by which to do so and both are shown.
```python
>>> from langchain.vectorstores.redis import RedisFilter, RedisNum, RedisText
>>> age_filter = RedisFilter.num("age") > 18
>>> age_filter = RedisNum("age") > 18 # equivalent
>>> results = rds.similarity_search(query, filter=age_filter)
>>> print(len(results))
3
>>> job_filter = RedisFilter.text("job") == "engineer"
>>> job_filter = RedisText("job") == "engineer" # equivalent
>>> results = rds.similarity_search(query, filter=job_filter)
>>> print(len(results))
2
# fuzzy match text search
>>> job_filter = RedisFilter.text("job") % "eng*"
>>> results = rds.similarity_search(query, filter=job_filter)
>>> print(len(results))
2
# combined filters (AND)
>>> combined = age_filter & job_filter
>>> results = rds.similarity_search(query, filter=combined)
>>> print(len(results))
1
# combined filters (OR)
>>> combined = age_filter | job_filter
>>> results = rds.similarity_search(query, filter=combined)
>>> print(len(results))
4
```
All the above filter results can be checked against the data above.
### TODO
- [x] more tests
- [x] docstrings
- [x] docs
### Other
- Issue: #3967
- Dependencies: No added dependencies
- Tag maintainer: @hwchase17 @baskaryan @rlancemartin
- Twitter handle: @sampartee
|
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PR_kwDOIPDwls5W90tk
| 8,610 |
improve the performance of base.py
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[] | null | 3 | 2023-08-02T03:07:16 | 2023-09-28T00:36:03 | 2023-09-28T00:36:03 |
CONTRIBUTOR
| null |
This removes the use of the intermediate df list and directly concatenates the dataframes if path is a list of strings. The pd.concat function combines the dataframes efficiently, making it faster and more memory-efficient compared to appending dataframes to a list.
<!-- 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),
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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:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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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_kwDOIPDwls5W9jpA
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[Opensearch] : Fix the service validation in http_auth
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[] | null | 1 | 2023-08-02T01:35:58 | 2023-08-02T08:16:38 | 2023-08-02T08:16:38 |
CONTRIBUTOR
| null |
### Description
OpenSearch supports validation using both Master Credentials (Username and password) and IAM. For Master Credentials users will not pass the argument `service` in `http_auth` and the existing code will break. To fix this, I have updated the condition to check if service attribute is present in http_auth before accessing it.
### Maintainers
@baskaryan @navneet1v
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Enterprise support form in airtable
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COLLABORATOR
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Re-structure agent docs
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[] | null | 3 | 2023-08-02T00:01:41 | 2023-08-16T22:02:57 | 2023-08-16T22:02:57 |
COLLABORATOR
| null |
1/ Update overview page for agents
2/ Move agent docs from use-cases to modules and consolidate there
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PR_kwDOIPDwls5W9SEI
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fix issue #8556 by restructure the metadata links
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[] | null | 4 | 2023-08-01T23:52:09 | 2023-08-12T20:45:46 | 2023-08-12T20:45:46 |
NONE
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- Description: restructure the metadata links from {str, list}, and flatten it to {str, str} which can be supported by Chroma vector store. The former format for links loaded from markdown file is not supported by Chroma.
- Issue: the issue #8556
- Tag maintainer: @rlancemartin
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PR_kwDOIPDwls5W9O4o
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normalized openai embeddings embed_query
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[] | null | 3 | 2023-08-01T23:33:27 | 2023-11-03T19:16:01 | 2023-08-02T00:12:11 |
COLLABORATOR
| null |
we weren't normalizing when embedding queries
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PR_kwDOIPDwls5W9Iwm
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Issue #8584 Fix GPTCache with ChatOpenAI
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[] | null | 1 | 2023-08-01T23:00:31 | 2023-08-16T20:02:10 | 2023-08-16T20:02:06 |
CONTRIBUTOR
| null |
Fixes Issue #8584
No new dependencies have been added.
@baskaryan
Also, I would like to ask, what's the process for getting the `expert` role on the Langchain discord and would I possibly qualify? Thanks.
|
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PR_kwDOIPDwls5W9DVm
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feat: Add Amazon Textract as document loader
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CONTRIBUTOR
| null |
Description: Adding support for [Amazon Textract](https://aws.amazon.com/textract/) as a PDF document loader
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. -> checked and worked fine
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PR_kwDOIPDwls5W8-63
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Add firestore_client param to FirestoreChatMessageHistory if caller already has one; also lets them specify GCP project, etc.
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[] | null | 3 | 2023-08-01T22:08:54 | 2023-08-01T22:42:14 | 2023-08-01T22:42:13 |
CONTRIBUTOR
| null |
Existing implementation requires that you install `firebase-admin` package, and prevents you from using an existing Firestore client instance if available.
This adds optional `firestore_client` param to `FirestoreChatMessageHistory`, so users can just use their existing client/settings. If not passed, existing logic executes to initialize a `firestore_client`.
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