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Introduces `StopSequenceAwareStreamer` that enhances streaming control by allowing end users to specify arbitrary stop sequences spanning multiple tokens, without knowledge of the tokenizer. This generalizes current functionality, adding capabilities without changing API. End users' current implementations will work the same while allowing longer stop sequences.
- **Issue:** -
- **Dependencies:** None
- **Twitter handle:** https://twitter.com/TheRealABook
Happy Friday <:)
This is still a draft in the sense that I would welcome your thoughts on: 1) where this logic would best be centralized. 2) which LLMs that we want to implement this for.
I've tested this out locally but will happily add unit tests for the components once we firm up the above two points!
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- Removed the `Google Drive toolkit` page (it shouldn't be a toolkit but tool)
- Removed the correspondent reference in the Google platform page
- Redirected the removed page to the tool page. | {
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- **Issue:** Some marketing opportunities, lodging PR and awaiting later discussions.
-
This PR is intended to be merged when decisions settle/hopefully after further considerations. Submitting as Draft for now. Nobody @'d yet. | {
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] | closed | false | null | [] | null | 1 | 2023-12-15T21:23:13 | 2023-12-15T22:25:11 | 2023-12-15T22:18:53 | COLLABORATOR | null | Updated the page title. It was inconsistent.
Updated page with links; description and setting details. | {
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] | open | false | null | [] | null | 1 | 2023-12-15T20:51:06 | 2023-12-15T20:55:50 | null | NONE | null | ### System Info
Hi,
I am trying to save the ParentDocumentRetriever after adding documents data to it. Since there a lot of documents that needs to be added, its not possible to do it every time. I tried saving via pickle but got the error: TypeError: cannot pickle 'sqlite3.Connection' object
Is there any way to save the retreiver and load it at the inference time?
### 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
retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter,
search_kwargs={"k": 3}
)
retriever.add_documents(docs, ids=None)
import pickle
with open('ParentDocumentRetriever.pkl', 'wb') as f:
pickle.dump(retriever, f, protocol = pickle.HIGHEST_PROTOCOL)
### Expected behavior
Provide a save to local option for the retreiver with added data | {
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] | closed | false | null | [] | null | 2 | 2023-12-15T20:37:27 | 2023-12-17T20:55:50 | 2023-12-17T20:55:49 | COLLABORATOR | null | Builds out a developer documentation section in the docs
- Links it from contributing.md
- Adds an initial guide on how to contribute an integration | {
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] | closed | false | null | [] | null | 5 | 2023-12-15T20:26:57 | 2023-12-16T13:38:13 | 2023-12-16T13:37:56 | CONTRIBUTOR | null | - **Description:** `ChatMistralAI` integration for https://docs.mistral.ai/api
- **Dependencies:** Must install the `mistralai` package
- **Tag maintainer:** @hwchase17
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] | closed | false | null | [] | null | 1 | 2023-12-15T19:53:47 | 2023-12-18T16:58:27 | 2023-12-17T22:54:48 | COLLABORATOR | null | [ScaNN](https://python.langchain.com/docs/integrations/providers/scann) and [DynamoDB](https://python.langchain.com/docs/integrations/platforms/aws#aws-dynamodb) pages in `providers` are redundant because we have those references in the Google and AWS platform pages. It is confusing.
- I removed unnecessary pages, redirected files to new nams; | {
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Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
Update LLMand Chat model to use new api version | {
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- Recreated a provide page
- Added missed links and descriptions
- Compound information about vector store from two pages into one
- Fixed file name | {
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- renamed `excel` and `onenote` file names | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14770 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14770/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14770/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14770/events | https://github.com/langchain-ai/langchain/pull/14770 | 2,044,154,998 | PR_kwDOIPDwls5iIj5V | 14,770 | [Documentation] Updates to NVIDIA Playground/Foundation Model naming.… | {
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- **Description:** Minor updates per marketing requests. Namely, name decisions (AI Foundation Models / AI Playground)
- **Tag maintainer:** @hinthornw
Do want to pass around the PR for a bit and ask a few more marketing questions before merge, but just want to make sure I'm not working in a vacuum. No major changes to code functionality intended; the PR should be for documentation and only minor tweaks.
Note: QA model is a bit borked across staging/prod right now. Relevant teams have been informed and are looking into it, and I'm placeholdered the response to that of a working version in the notebook.
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- https://cloud.google.com/python/docs/reference/aiplatform/latest/vertexai.language_models.TextEmbeddingInput
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https://api.github.com/repos/langchain-ai/langchain/issues/14766 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14766/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14766/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14766/events | https://github.com/langchain-ai/langchain/pull/14766 | 2,043,940,791 | PR_kwDOIPDwls5iH00M | 14,766 | Update propositional-retrieval template | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14765 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14765/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14765/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14765/events | https://github.com/langchain-ai/langchain/pull/14765 | 2,043,875,052 | PR_kwDOIPDwls5iHmXS | 14,765 | Add safe lookup to OpenAI response adapter | {
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## Description
Similar to https://github.com/langchain-ai/langchain/issues/5861, I've experienced `KeyError`s resulting from unsafe lookups in the `convert_dict_to_message` function in [this file](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/adapters/openai.py). While that issue focused on `KeyError 'content'`, I've opened another issue (#14764) about how the problem still exists in the same function but with `KeyError 'role'`. The fix for #5861 only added a safe lookup to the specific line that was giving them trouble.. This PR fixes the unsafe lookup in the rest of the function but the problem still exists across the repo.
## Issues
* #14764
* #5861
## Dependencies
* None
## Checklist
[x] make format
[x] make lint
[ ] make test - Results in `make: *** No rule to make target 'test'. Stop.`
## Maintainers
* @hinthornw | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14764 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14764/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14764/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14764/events | https://github.com/langchain-ai/langchain/issues/14764 | 2,043,873,642 | I_kwDOIPDwls550wlq | 14,764 | KeyError 'role' in OpenAI Adapter | {
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] | closed | false | null | [] | null | 3 | 2023-12-15T14:57:13 | 2024-01-06T01:49:24 | 2024-01-05T14:14:27 | CONTRIBUTOR | null | ### System Info
Python 3.10
LangChain 0.0.348
### Who can help?
@hwchase17 @hin
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.chat_models import ChatOpenAI
from langchain.memory import ChatMessageHistory
history = ChatMessageHistory()
history.add_user_message("Tell me anything in ten words or less.")
params = {
"model": "gpt-4-1106-preview",
"temperature": 0.5,
"max_tokens": 1000,
}
llm = ChatOpenAI(**params)
print(llm(history.messages))
```
This will **occasionally** yield the following error:
```
File "/.../.venv/lib/python3.10/site-packages/langchain/adapters/openai.py", line 74, in convert_dict_to_message
role = _dict["role"]
KeyError: 'role'
```
### Expected behavior
* Safe lookups ensure the response is passed back even if the role isn't attached | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14763 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14763/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14763/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14763/events | https://github.com/langchain-ai/langchain/issues/14763 | 2,043,790,550 | I_kwDOIPDwls550cTW | 14,763 | Add support for Mistral AI service | {
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] | closed | false | null | [] | null | 2 | 2023-12-15T14:05:31 | 2023-12-19T19:31:27 | 2023-12-19T19:31:27 | NONE | null | ### Feature request
Add support for the new service from Mistral AI.
They provide a [python client](https://docs.mistral.ai/platform/client) with streaming tokens or we can use the simple [RestAPI](https://docs.mistral.ai/)
### Motivation
Would be great if we add the new service from Mistral!
### Your contribution
I don't have much time right now but i'd like to follow the implementation for the future | {
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] | closed | false | null | [] | null | 1 | 2023-12-15T14:03:23 | 2023-12-15T21:10:05 | 2023-12-15T21:10:05 | CONTRIBUTOR | null | ### Issue with current documentation:
I've got this code:
```python
llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.1",
model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
# Vectorstore
vectorstore = Chroma(
embedding_function=HuggingFaceEmbeddings(
model_name="all-MiniLM-L6-v2"), persist_directory="./chroma_db_oai"
)
search = DuckDuckGoSearchAPIWrapper(max_results=max_num_results,
region="jp-ja",
time="d")
user_input = "Which are the most demanded jobs for foreigner people that don't speak Japanese?"
web_research_retriever = WebResearchRetriever.from_llm(
vectorstore=vectorstore,
llm=llm,
search=search)
```
but I get this error:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[44], line 15
12 search = DuckDuckGoSearchAPIWrapper()
13 user_input = "Which are the most demanded jobs for foreigner people that don't speak Japanese?"
---> 15 web_research_retriever = WebResearchRetriever.from_llm(
16 vectorstore=vectorstore,
17 llm=llm,
18 search=search)
20 # Run
21 docs = web_research_retriever.get_relevant_documents(user_input)
File ~/anaconda3/envs/jobharbor/lib/python3.10/site-packages/langchain/retrievers/web_research.py:130, in WebResearchRetriever.from_llm(cls, vectorstore, llm, search, prompt, num_search_results, text_splitter)
123 # Use chat model prompt
124 llm_chain = LLMChain(
125 llm=llm,
126 prompt=prompt,
127 output_parser=QuestionListOutputParser(),
128 )
--> 130 return cls(
131 vectorstore=vectorstore,
132 llm_chain=llm_chain,
133 search=search,
134 num_search_results=num_search_results,
135 text_splitter=text_splitter,
136 )
File ~/anaconda3/envs/jobharbor/lib/python3.10/site-packages/langchain_core/load/serializable.py:97, in Serializable.__init__(self, **kwargs)
96 def __init__(self, **kwargs: Any) -> None:
---> 97 super().__init__(**kwargs)
98 self._lc_kwargs = kwargs
File ~/anaconda3/envs/jobharbor/lib/python3.10/site-packages/pydantic/v1/main.py:341, in BaseModel.__init__(__pydantic_self__, **data)
339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
--> 341 raise validation_error
342 try:
343 object_setattr(__pydantic_self__, '__dict__', values)
ValidationError: 6 validation errors for WebResearchRetriever
search -> backend
extra fields not permitted (type=value_error.extra)
search -> max_results
extra fields not permitted (type=value_error.extra)
search -> region
extra fields not permitted (type=value_error.extra)
search -> safesearch
extra fields not permitted (type=value_error.extra)
search -> source
extra fields not permitted (type=value_error.extra)
search -> time
extra fields not permitted (type=value_error.extra)
```
I wonder if the webresearch retriever works also with non-google search engines..
Thank you in advance
### Idea or request for content:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14761 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14761/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14761/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14761/events | https://github.com/langchain-ai/langchain/pull/14761 | 2,043,766,894 | PR_kwDOIPDwls5iHOrG | 14,761 | [Partner] Nvidia Triton updates for PR | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14760 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14760/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14760/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14760/events | https://github.com/langchain-ai/langchain/issues/14760 | 2,043,732,734 | I_kwDOIPDwls550OL- | 14,760 | Table 'langchain_pg_collection' is already defined for this MetaData instance. Specify 'extend_existing=True' | {
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] | closed | false | null | [] | null | 4 | 2023-12-15T13:29:34 | 2024-01-03T09:19:59 | 2023-12-15T18:19:15 | NONE | null | ### System Info
langchain: 0.0.39
python: 3.11
OS: MacOS Sonoma 14.2
### Who can help?
@hwchase17 @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Load two collections using PGVector like this:
collection1 = PGVector.from_existing_index(
embedding=embeddings,
collection_name="collection1",
pre_delete_collection=False,
distance_strategy=DistanceStrategy.COSINE,
connection_string=CONNECTION_STRING,
)
collection2 = PGVector.from_existing_index(
embedding=embeddings,
collection_name="collection1",
pre_delete_collection=False,
distance_strategy=DistanceStrategy.COSINE,
connection_string=CONNECTION_STRING,
)
`collection1` works fine, but while initializing collection2, I get the error
`Table 'langchain_pg_collection' is already defined for this MetaData instance. Specify 'extend_existing=True'`
Setting `extend_existing=True` doesn't seem to work - I think this is a SQLAlchemy error.
### Expected behavior
The above code should just work without error.
This was working in previous versions: 0.0.310 this code works, but shows an error in 0.0.390.
If there are multiple connections happening to the index, then the documentation doesn't talk about how to handle multiple connections. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14759 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14759/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14759/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14759/events | https://github.com/langchain-ai/langchain/pull/14759 | 2,043,626,696 | PR_kwDOIPDwls5iGwGg | 14,759 | [FEATURE] Add Aphrodite Engine support | {
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] | closed | false | null | [] | null | 2 | 2023-12-15T12:16:16 | 2023-12-20T06:16:58 | 2023-12-20T06:16:58 | CONTRIBUTOR | null | This PR adds support for PygmalionAI's [Aphrodite Engine](https://github.com/PygmalionAI/aphrodite-engine), based on vLLM's attention mechanism. At the moment, this PR does not include support for the API servers, but they will be added in a later PR.
The only dependency as of now is `aphrodite-engine==0.4.2`. We pin the version to prevent breakage due to changes in the aphrodite-engine library.
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https://api.github.com/repos/langchain-ai/langchain/issues/14757 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14757/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14757/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14757/events | https://github.com/langchain-ai/langchain/issues/14757 | 2,043,603,478 | I_kwDOIPDwls55zuoW | 14,757 | Langserve example from Quickstart tutorial not working | {
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] | open | false | null | [] | null | 2 | 2023-12-15T12:00:17 | 2023-12-15T14:36:35 | null | NONE | null | ### System Info
Name Version Build Channel
langchain 0.0.350 pypi_0 pypi
langchain-cli 0.0.19 pypi_0 pypi
langchain-community 0.0.3 pypi_0 pypi
langchain-core 0.1.1 pypi_0 pypi
langchain-experimental 0.0.47 pypi_0 pypi
python 3.12.0 h47c9636_0_cpython conda-forge
System: macOS 14.2 (Apple M1 chip)
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Go to https://python.langchain.com/docs/get_started/quickstart
2. Do all the package installations as guided.
3. Copy the python code under the section 'Serving with Langserve' and save it to a file `serve.py`
4. Execute the file `python serve.py`
5. Open `http://localhost:8000` in browser
### Expected behavior
Expected to see Langserve UI. Got the following error instead.
---
LANGSERVE: Playground for chain "/category_chain/" is live at:
LANGSERVE: │
LANGSERVE: └──> /category_chain/playground/
LANGSERVE:
LANGSERVE: See all available routes at /docs/
LANGSERVE: ⚠️ Using pydantic 2.5.2. OpenAPI docs for invoke, batch, stream, stream_log endpoints will not be generated. API endpoints and playground should work as expected. If you need to see the docs, you can downgrade to pydantic 1. For example, `pip install pydantic==1.10.13`. See https://github.com/tiangolo/fastapi/issues/10360 for details.
INFO: Application startup complete.
INFO: Uvicorn running on http://localhost:8000 (Press CTRL+C to quit)
INFO: ::1:58516 - "GET / HTTP/1.1" 404 Not Found
INFO: ::1:58516 - "GET /favicon.ico HTTP/1.1" 404 Not Found
^CINFO: Shutting down
INFO: Waiting for application shutdown.
INFO: Application shutdown complete.
INFO: Finished server process [91610] | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14754 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14754/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14754/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14754/events | https://github.com/langchain-ai/langchain/issues/14754 | 2,043,485,623 | I_kwDOIPDwls55zR23 | 14,754 | Issue: I'm currently working on a project where I need to fetch all the sub-URLs from a website using Langchain. | {
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] | open | false | null | [] | null | 4 | 2023-12-15T10:52:12 | 2023-12-16T10:32:14 | null | NONE | null | ### Issue you'd like to raise.
I'm currently working on a project where I need to fetch all the sub-URLs from a website using Langchain.
How can we achieve this, below is my code
`from langchain.tools.playwright import ExtractHyperlinksTool, NavigateTool
# Initialize the tools
navigate_tool = NavigateTool()
extract_hyperlinks_tool = ExtractHyperlinksTool()
# Navigate to the website
navigate_tool.navigate("https://www.example.com")
# Extract all hyperlinks
hyperlinks = extract_hyperlinks_tool.extract()
# Print all hyperlinks
for link in hyperlinks:
print(link)`
I am getting below mentioned error
File "/home/hs/env/lib/python3.8/site-packages/langchain_core/load/serializable.py", line 97, in init
super().init(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.init
pydantic.error_wrappers.ValidationError: 1 validation error for NavigateTool
root
Either async_browser or sync_browser must be specified. (type=value_error)
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14753 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14753/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14753/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14753/events | https://github.com/langchain-ai/langchain/issues/14753 | 2,043,356,756 | I_kwDOIPDwls55yyZU | 14,753 | ImportError: cannot import name 'GoogleGenerativeAI' from 'langchain_google_genai' ( from langchain_google_genai import GoogleGenerativeAI ) | {
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] | open | false | null | [] | null | 4 | 2023-12-15T10:01:36 | 2023-12-19T03:00:59 | null | NONE | null | ### System Info
- Python 3.11.7
- Windows 64bit
- langchain_google_genai==0.0.4
- langchain==0.0.350
- pymongo==4.6.0
### Who can help?
@sbusso
@jarib
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Install langchain_google_genai using `pip install -U langchain-google-genai`
2. Then, `from langchain_google_genai import GoogleGenerativeAI`
3. This will produce error.
### Expected behavior
I want to use this model for Google AI, but not able to access it | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14752 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14752/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14752/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14752/events | https://github.com/langchain-ai/langchain/issues/14752 | 2,043,013,698 | I_kwDOIPDwls55xepC | 14,752 | Every chain (LLMChain, ConversationalRetrievalChain etc) should return stream without CallbackHandler same as llm ChatModel | {
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] | open | false | null | [] | null | 2 | 2023-12-15T06:56:26 | 2023-12-19T05:05:24 | null | NONE | null | ### Feature request
All chains inherit from `Runnable` class and It has the `stream` method. But no chain has it's own `stream` method. That's why we have to pass a callback handler every time when work on streaming. We need to write additional logic to handle stream. There should be a functionality where we can just call the stream method and it will return the Generator same as ChatModel (ChatOpenAI).
### Motivation
I believe Chain is the core of LangChain. This is so much frustrating when work with stream.
Every time I work with streaming I have to write something like this. I have to run the qa task in the background using task or thread which is not suitable If I using a framework. Framework should handle this. New developers will struggle if they need to handle additional logic even though they used a framework.
```
class StreamingLLMCallbackHandler(BaseCallbackHandler):
"""Callback handler for streaming LLM responses to a queue."""
def __init__(self):
self._is_done = False
self._queue = Queue()
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Run on new LLM token. Only available when streaming is enabled."""
# print(token)
self._queue.put(EventData(content=token))
def on_llm_end(self, response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) -> Any:
"""Run when LLM ends running."""
self._queue.put(EventData(content=None, finish_reason='done'))
self._is_done = True
def add_new_token_to_stream(self, data: EventData, is_done=True):
self._queue.put(data)
self._is_done = is_done
@property
def stream_gen(self):
while not self._is_done or not self._queue.empty():
try:
delta: EventData = self._queue.get()
if delta.data.get('finish_reason') == 'error':
yield str(StreamData(event_data=delta, event=EnumStreamEventType.ERROR))
else:
yield str(StreamData(event_data=delta))
except Empty:
continue
class LangChainChatService(ChatBaseService):
def __init__(self, model: LangchainChatModel, tool_query=None):
super().__init__()
self.model = model
self.request_manager = api_request_manager_var.get()
self.tool_query = tool_query
def _qa_task(self, streaming_handler: StreamingLLMCallbackHandler, qa: BaseConversationalRetrievalChain, formatted_chat_history: list[BaseMessage]):
try:
question = self.tool_query or self.model.query
answer = qa.run(question=question, chat_history=formatted_chat_history)
self._publish_chat_history(answer)
except Exception as ex:
streaming_handler.add_new_token_to_stream(EventData(content=get_user_message_on_exception(ex), error=build_error_details(ex), finish_reason='error'))
logger.exception(ex)
async def stream_chat_async(self):
streaming_handler = StreamingLLMCallbackHandler()
try:
formatted_chat_history = [] if self.tool_query else self.get_formatted_chat_history(self.model.chat_history)
qa = self._get_qa_chain(callbacks=[streaming_handler])
asyncio.create_task(asyncio.to_thread(self._qa_task, streaming_handler, qa, formatted_chat_history))
return streaming_handler.stream_gen
except Exception as ex:
logger.exception(ex)
streaming_handler.add_new_token_to_stream(EventData(content=get_user_message_on_exception(ex), error=build_error_details(ex), finish_reason='error'))
return streaming_handler.stream_gen
```
### Your contribution
N/A | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14751 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14751/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14751/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14751/events | https://github.com/langchain-ai/langchain/issues/14751 | 2,042,960,303 | I_kwDOIPDwls55xRmv | 14,751 | cannot import name 'AzureChatopenAI' from 'langchain.chat_models' | {
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] | closed | false | null | [] | null | 1 | 2023-12-15T06:03:38 | 2023-12-15T06:11:27 | 2023-12-15T06:07:10 | NONE | null | ### System Info
ImportError: cannot import name 'AzureChatopenAI' from 'langchain.chat_models'
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.chat_models import AzureChatopenAI
### Expected behavior
from langchain.chat_models import AzureChatopenAI
llm = ChatOpenAI(
openai_api_base=url,
openai_api_version="2023-05-15",
deployment_name="gpt-3.5",
openai_api_key="test123456",
openai_api_type="azure",
) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14750 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14750/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14750/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14750/events | https://github.com/langchain-ai/langchain/issues/14750 | 2,042,951,433 | I_kwDOIPDwls55xPcJ | 14,750 | adapt chatglm output_parser | {
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] | open | false | null | [] | null | 1 | 2023-12-15T05:55:16 | 2023-12-15T06:05:08 | null | NONE | null | ### Feature request
i want to struct output result by langchain,my llm is chatglm,but most output parsers demo is rely on openai
### Motivation
retriever = db.as_retriever()
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type='stuff',
retriever=retriever,
verbose=True
)
def chat(question,history):
response = qa.run(question)
return response
demo = gr.ChatInterface(chat)
demo.launch(inbrowser=True)
### Your contribution
i want to struct output result by langchain,my llm is chatglm,but most output parsers demo is rely on openai | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14749 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14749/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14749/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14749/events | https://github.com/langchain-ai/langchain/pull/14749 | 2,042,862,938 | PR_kwDOIPDwls5iEHWF | 14,749 | Added: docs Headers to Steam Tool notebook steps | {
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] | closed | false | null | [] | null | 1 | 2023-12-15T04:12:19 | 2024-01-02T19:40:40 | 2024-01-02T00:21:41 | CONTRIBUTOR | null | Added some Headers in steam tool notebook to match consistency with the other toolkit notebooks
- Dependencies: no new dependencies
- Tag maintainer: @hwchase17, @baskaryan | {
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- **Twitter handle:** cbornet_
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It expects a "name" argument, which we aren't providing by default.
Alternative is to add the 'name' field directly to the message if people prefer. | {
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- **Issue:** This library is used inside dereference_refs inside langchain_community.agent_toolkits.openapi.spec. When I read in a yaml file which has references for "400", "401" etc; the line "out = out[component]" causes a KeyError. The isdigit() check ensures that if it is an integer like "400" or "401"; it converts it into integer before using it as a key to prevent the error.
- **Dependencies:** No dependencies
- **Tag maintainer:** @baskaryan | {
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I've been working with the `create_vectorstore_router_agent` function, particularly in conjunction with the `VectorStoreRouterToolkit`, and I've encountered a limitation that I believe could be an important area for enhancement.
Currently, the response from this function primarily includes the final processed answer to a query. However, it does not provide any details about the source documents or the similarity search performed within the vector store. In many applications, especially those that require a high degree of transparency and traceability of information, having access to the source documents is crucial.
The ability to see which documents were retrieved, along with their similarity scores or other relevant metadata, is invaluable. It helps in understanding the basis of the answers provided by the agent and is essential for verifying the relevance and accuracy of the information.
Therefore, I suggest enhancing the functionality of the toolkit to include an option to return detailed information about the retrieved documents in the response. This could be implemented as an optional feature that can be enabled as needed, depending on the specific requirements of the use case.
Such a feature would significantly enhance the utility and applicability of the Langchain library, particularly in scenarios where detailed source information is essential for validation, auditing, or explanatory purposes.
Thank you for considering this enhancement. I believe it would make a great addition to the capabilities of Langchain.
Best regards,
Sarath chennamsetty.
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https://api.github.com/repos/langchain-ai/langchain/issues/14741 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14741/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14741/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14741/events | https://github.com/langchain-ai/langchain/issues/14741 | 2,042,704,569 | I_kwDOIPDwls55wTK5 | 14,741 | Issue: Update the outdated hugging face client | {
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] | open | false | null | [] | null | 1 | 2023-12-15T00:36:29 | 2023-12-15T00:38:44 | null | NONE | null | ### Issue you'd like to raise.
Hugging face will deprecate the `InferenceApi` and move to `InferenceClient`. Can the langchain package also update the dependency in `HuggingFaceHub` accordingly? Refer to [migration docs](https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client) to migrate to new client.
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14740 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14740/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14740/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14740/events | https://github.com/langchain-ai/langchain/pull/14740 | 2,042,699,258 | PR_kwDOIPDwls5iDljA | 14,740 | Implement similarity_score_threshold for MongoDB Vector Store | {
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] | closed | false | null | [] | null | 1 | 2023-12-15T00:29:26 | 2023-12-16T00:49:21 | 2023-12-16T00:49:21 | CONTRIBUTOR | null | Adds the option for `similarity_score_threshold` when using `MongoDBAtlasVectorSearch` as a vector store retriever.
Example use:
```
vector_search = MongoDBAtlasVectorSearch.from_documents(...)
qa_retriever = vector_search.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={
"score_threshold": 0.5,
}
)
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=qa_retriever,
)
docs = qa({"query": "..."})
```
I've tested this feature locally, using a MongoDB Atlas Cluster with a vector search index. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14739 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14739/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14739/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14739/events | https://github.com/langchain-ai/langchain/pull/14739 | 2,042,675,737 | PR_kwDOIPDwls5iDgkJ | 14,739 | infra: docs build install community editable | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14736 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14736/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14736/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14736/events | https://github.com/langchain-ai/langchain/pull/14736 | 2,042,623,154 | PR_kwDOIPDwls5iDVH_ | 14,736 | Jacob/add hf chat wrapper | {
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] | closed | false | null | [] | null | 3 | 2023-12-14T22:52:29 | 2023-12-21T17:28:31 | 2023-12-21T17:28:30 | CONTRIBUTOR | null | Builds on #14040 with community refactor merged and notebook updated.
Note that with this refactor, models will be imported from `langchain_community.chat_models.huggingface` rather than the main `langchain` repo.
CC @baskaryan @andrewrreed | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14735 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14735/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14735/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14735/events | https://github.com/langchain-ai/langchain/pull/14735 | 2,042,611,441 | PR_kwDOIPDwls5iDSjZ | 14,735 | Revert "[Hub|tracing] Tag hub prompts" | {
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- **Issue:**
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@jdye64 I've got some questions and need your help to validate that this works. Will add some comments below.
- [] Add README
- [X] Add doc notebook
- [X] Add simple LLM integration | {
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- **Description:** removing a trailing "\`" character in a `!pip install ...` command
- **Issue:** n/a
- **Dependencies:** n/a
- **Tag maintainer:** n/a
- **Twitter handle:** n/a
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Replace this entire comment with:
- **Description:** a small fix in branding
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https://api.github.com/repos/langchain-ai/langchain/issues/14727 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14727/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14727/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14727/events | https://github.com/langchain-ai/langchain/pull/14727 | 2,042,341,366 | PR_kwDOIPDwls5iCWWG | 14,727 | docs[patch]: fix databricks metadata | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14726 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14726/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14726/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14726/events | https://github.com/langchain-ai/langchain/pull/14726 | 2,042,230,909 | PR_kwDOIPDwls5iB-JT | 14,726 | community[patch]: fix pgvector sqlalchemy | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14725 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14725/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14725/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14725/events | https://github.com/langchain-ai/langchain/issues/14725 | 2,042,229,871 | I_kwDOIPDwls55ufRv | 14,725 | Google Drive Loader: The code in the Official Doc does not work; and Setting environment variable GOOGLE_APPLICATION_CREDENTIALS is important, though its value is not | {
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] | open | false | null | [] | null | 1 | 2023-12-14T18:16:28 | 2023-12-14T18:25:25 | null | CONTRIBUTOR | null | ### System Info
Langchain Version - 0.0.348
OS or Platform Version - Windows 11
Python Version - 3.11.5
Conda Version - 23.7.4
### Who can help?
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
## BRIEF
* [Issue 1] When using Google Drive Loader to load Google Docs, I encountered several errors following the offcial documentation. I ran my code on 3 different platforms - Windows, Kaggle Notebooks, and CodeSandbox (Linux). [Here is the code](https://python.langchain.com/docs/integrations/document_loaders/google_drive#instructions-for-ingesting-your-google-docs-data) that does not work.
* [Issue 2] However, after playing with the code for a while, I was able to successfully authenticate with Google and load the docs. But, this brings up another issue to our notice. The environment variable needs to be set, but its value can be any string.
## REPRODUCTION STEPS
<details>
<summary>Pre requisites</summary>
1. Complete the [Prerequisites for the GoogleDriveLoader.](https://python.langchain.com/docs/integrations/document_loaders/google_drive#prerequisites)
2. Create a Google Docs file and copy its document ID. [How to find the document ID.](https://python.langchain.com/docs/integrations/document_loaders/google_drive#instructions-for-ingesting-your-google-docs-data).
3. Please read the section mentioned in point number 2.
4. Create an empty directory on Windows for this issue and place the `credentials.json` file there, recieved from Google Cloud.
5. Create main.py file, to write the below 3 programs, in this directory.
6. I run the program with ```python main.py```
</details>
### FIRST TRY
Follow the documentation
```python
# Set the Path to the credentials and authentication token
credentials_path = "credentials.json"
token_path = "token.json"
# Create the loader object (no environment variable is set)
loader = GoogleDriveLoader(
document_ids=["Your Document ID here"],
credentials_path=credentials_path,
token_path=token_path
)
# Call the loader, after the above code:
loader.load()
```
<details>
<summary>Output - Errors</summary>
Windows and CodeSandbox (Linux):
> raise exceptions.DefaultCredentialsError(_CLOUD_SDK_MISSING_CREDENTIALS)
google.auth.exceptions.DefaultCredentialsError: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.
Kaggle Notebooks:
> RefreshError: Failed to retrieve http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/?recursive=true from the Google Compute Engine metadata service. Compute Engine Metadata server unavailable
</details>
### SECOND TRY
Documentation mentions that if I get RefreshError, I should not pass the credentials path in the constructor, rather in the env var.
```python
# Set the env var. This path was declared before
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentials_path # change
loader = GoogleDriveLoader(
document_ids=["Your Document ID here"],
# credentials_path=credentials_path, # this one is removed
token_path=token_path
)
```
<details>
<summary>Output - Errors</summary>
Kaggle Notebooks and CodeSandbox (Linux)
> FileNotFoundError: [Errno 2] No such file or directory: '/root/.credentials/credentials.json'
Windows
> FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Username\\.credentials\\credentials.json'
</details>
### THIRD TRY: MY APPROACH
The above error means, if we skip the credentials_path param in the constructor, it uses the default path and does not uses the path provided in the env var.
Now, If I set GOOGLE_APPLICATION_CREDENTIALS environment variable to ANY string and pass the path in the constructor:
```python
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "" # change: set the env var to any str
loader = GoogleDriveLoader(
document_ids=["Your Document ID here"],
credentials_path=credentials_path, # this one is added again
token_path=token_path
)
```
<details>
<summary>Output - user authentication to load the gdoc</summary>
Windows and Kaggle Notebook:
> Please visit this URL to authorize this application: https://accounts.google.com/o...
CodeSandbox (Linux) 😂:
> webbrowser.Error: could not locate runnable browser
</details>
## CONCLUSION
* [Issue 1] The code provided in the documentation does not work. A/c to the docs:
* If the env var is **not set**, and the path is **passed** in the constructor, the code gives errors.
* If the env var is **set** as the path, and the path is **not passed** in constructor, the code gives errors.
* [Issue 2] The env var needs to be set, but its value is not important. As per my approach:
* If the env var is **set** as ANYTHING, and the path is **passed** in the constructor, the code works.
### Expected behavior
I do not know what to expect, because the code given in the official documentation is not working correctly.
Suggestion:
However, I would suggest that, if there is no need for the env var, maybe we can remove the step of setting it.
This will resolve the second issue. For first issue, remaining would be to correct the documentation. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14724 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14724/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14724/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14724/events | https://github.com/langchain-ai/langchain/issues/14724 | 2,042,218,831 | I_kwDOIPDwls55uclP | 14,724 | AttributeError: module 'google.generativeai' has no attribute 'GenerativeModel' | {
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] | closed | false | null | [] | null | 9 | 2023-12-14T18:10:24 | 2024-01-07T20:45:38 | 2023-12-15T23:55:03 | NONE | null | ### System Info
Similar issue, when tried to work with Gemini.Ai
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
AttributeError: module 'google.generativeai' has no attribute 'GenerativeModel'
### Expected behavior
just loaded those images that i have uploaded for the prompt | {
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- **Tag maintainer:** @hinthornw
Do want to pass around the PR for a bit and ask a few more marketing questions before merge, but just want to make sure I'm not working in a vacuum. No major changes to code functionality intended; the PR should be for documentation and only minor tweaks.
Note: QA model is a bit borked across staging/prod right now. Relevant teams have been informed and are looking into it, and I'm placeholdered the response to that of a working version in the notebook.
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https://api.github.com/repos/langchain-ai/langchain/issues/14722 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14722/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14722/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14722/events | https://github.com/langchain-ai/langchain/pull/14722 | 2,042,113,310 | PR_kwDOIPDwls5iBkQE | 14,722 | [Workflows] Add nvidia-aiplay to _release.yml | {
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In the future will want to have a script to automate this | {
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Thanks!
@hwchase17 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14718 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14718/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14718/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14718/events | https://github.com/langchain-ai/langchain/pull/14718 | 2,041,893,327 | PR_kwDOIPDwls5iA0Df | 14,718 | Add support to parse VertexAI Model garden Llama2 model output | {
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] | null | 2 | 2023-12-14T15:05:12 | 2024-01-04T15:20:57 | null | CONTRIBUTOR | null | - **Description:** The output of Llama2 models in GCP VertexAI Model garden is not compatible with the generic VertexAiModelGarden handler class. This PR adds a class to handle/ properly parse the output returned by those models. The handler also adds support for storing/ passing parameters to the model that is consistent with the core VertexAI LLM models.
- **Dependencies:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/14717 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14717/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14717/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14717/events | https://github.com/langchain-ai/langchain/issues/14717 | 2,041,882,866 | I_kwDOIPDwls55tKjy | 14,717 | `LLMChain` does not support `ChatGoogleGenerativeAI` | {
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] | closed | false | null | [] | null | 5 | 2023-12-14T14:59:49 | 2023-12-16T10:53:50 | 2023-12-15T12:25:32 | NONE | null | ### System Info
langchain==0.0.330
langchain-core==0.1.0
langchain-google-genai==0.0.3
python: 3.11
### Who can help?
@agola11 @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain_google_genai import ChatGoogleGenerativeAI
llm = ChatGoogleGenerativeAI(
model="gemini-pro",
google_api_key=google_api_key,
)
return LLMChain(llm=llm, prompt=prompt, verbose=verbose)
```
Error:
```
pydantic.error_wrappers.ValidationError: 2 validation errors for LLMChain
llm
instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)
llm
instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)
```
### Expected behavior
I expect LLMChain to run as usual with this new chat model. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14716 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14716/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14716/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14716/events | https://github.com/langchain-ai/langchain/pull/14716 | 2,041,880,490 | PR_kwDOIPDwls5iAxTI | 14,716 | pipeline test | {
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] | closed | false | null | [] | null | 1 | 2023-12-14T14:58:43 | 2023-12-14T15:04:40 | 2023-12-14T14:58:47 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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If you're adding a new integration, please include:
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https://api.github.com/repos/langchain-ai/langchain/issues/14715 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14715/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14715/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14715/events | https://github.com/langchain-ai/langchain/issues/14715 | 2,041,879,113 | I_kwDOIPDwls55tJpJ | 14,715 | Ollama max tokens parameter | {
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] | open | false | null | [] | null | 1 | 2023-12-14T14:58:07 | 2023-12-14T15:06:12 | null | NONE | null | ### Discussed in https://github.com/langchain-ai/langchain/discussions/14714
<div type='discussions-op-text'>
<sup>Originally posted by **ciliamadani** December 14, 2023</sup>
I'm currently in the process of developing a chatbot utilizing Langchain and the Ollama (llama2 7b model). My objective is to allow users to control the number of tokens generated by the language model (LLM).
In the Ollama documentation, I came across the parameter 'num_predict,' which seemingly serves this purpose. However, when using Ollama as a class from Langchain, I couldn't locate the same parameter. Consequently, I've been attempting to pass it as metadata. Unfortunately, even when setting this parameter to a low value, such as 50, the LLM continues to generate more tokens than expected.
I'm wondering if you have any insights on how I can effectively control the number of generated tokens when using Ollama as a Langchain class?
My current code
llm = Ollama(model="llama2:7b-chat-q4_0",callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),temperature=temperature,repeat_penalty=1.19,top_p=top_p,repeat_last_n=-1,metadata={"num_predict": num_predict})
Thank you.</div> | {
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] | closed | false | null | [] | null | 1 | 2023-12-14T14:52:25 | 2023-12-16T00:00:56 | 2023-12-16T00:00:55 | COLLABORATOR | null | Support [LLaVA](https://ollama.ai/library/llava):
* Upgrade Ollama
* `ollama pull llava`
Ensure compatibility with [image prompt template](https://github.com/langchain-ai/langchain/pull/14263) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14712 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14712/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14712/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14712/events | https://github.com/langchain-ai/langchain/issues/14712 | 2,041,683,363 | I_kwDOIPDwls55sZ2j | 14,712 | TypeError: Chain.__call__() got an unexpected keyword argument 'stop' | {
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] | open | false | null | [] | null | 1 | 2023-12-14T13:18:03 | 2023-12-14T13:28:09 | null | NONE | null | ### Issue you'd like to raise.
Hi all,
trying to use Langchain with HuggingFace model and Embeddings.
Am new to Langchain so any pointers welcome.
`import os
os.environ['HUGGINGFACEHUB_API_TOKEN']=myToken
#required to avoid certificate issue
os.environ['CURL_CA_BUNDLE'] = ''
from langchain import PromptTemplate, HuggingFaceHub, LLMChain
#Build prompt
template = """
Question: {question}
Answer: Let's think of the best answer, with arguments."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain=LLMChain(prompt=prompt,
llm=HuggingFaceHub(repo_id="google/flan-t5-xxl",
model_kwargs={"temperature":0.5,
"max_length":64}))
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("https://www.ofgem.gov.uk/sites/default/files/docs/2018/10/rec_v1.0_main_body.pdf")
pages = loader.load_and_split()
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
doc_embed = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
docsearch = Chroma.from_documents(pages, doc_embed)
qa_chain = RetrievalQA.from_chain_type(
llm_chain,
retriever=docsearch.as_retriever()
)
output = qa_chain.run("what is the retail energy code?")`
I get the following error:
---> 89 return self(
90 input,
91 callbacks=config.get("callbacks"),
92 tags=config.get("tags"),
93 metadata=config.get("metadata"),
94 run_name=config.get("run_name"),
95 **kwargs,
96 )
TypeError: Chain.__call__() got an unexpected keyword argument 'stop'
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14711 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14711/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14711/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14711/events | https://github.com/langchain-ai/langchain/issues/14711 | 2,041,671,633 | I_kwDOIPDwls55sW_R | 14,711 | 'google.generativeai' has no attribute 'GenerativeModel' | {
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] | open | false | null | [] | null | 22 | 2023-12-14T13:11:09 | 2023-12-19T19:45:16 | null | NONE | null | ### System Info
Anaconda 23.7.2
Windows 11
Python 3.11.5
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Method 1
```
def generate_lecture(topic:str, context:str):
template ="""
As an accomplished university professor and expert in {topic}, your task is to develop an elaborate, exhaustive, and highly detailed lecture on the subject.
Remember to generate content ensuring both novice learners and advanced students can benefit from your expertise.
while leveraging the provided context
Context: {context} """
prompt = ChatPromptTemplate.from_template(template)
model = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=palm_api_key)
response = model.invoke(template)
return response.content
```
Method 2
```
def generate_lecture(topic:str, context:str):
template ="""
As an accomplished university professor and expert in {topic}, your task is to develop an elaborate, exhaustive, and highly detailed lecture on the subject.
Remember to generate content ensuring both novice learners and advanced students can benefit from your expertise.
while leveraging the provided context
Context: {context} """
prompt = ChatPromptTemplate.from_template(template)
model = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=palm_api_key)
chain = prompt| model | StrOutputParser()
response = chain.invoke({"topic":topic, "context":context})
return response.
```
Result we receive on execttion of method 1 and method 2 is
```
File "C:\Users\ibokk\RobotForge\mvp\service\llm.py", line 80, in generate_lecture
model = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=palm_api_key)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ibokk\anaconda3\envs\robotforge\Lib\site-packages\langchain_core\load\serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "pydantic\main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic\main.py", line 1102, in pydantic.main.validate_model
File "C:\Users\ibokk\anaconda3\envs\robotforge\Lib\site-packages\langchain_google_genai\chat_models.py", line 502, in validate_environment
values["_generative_model"] = genai.GenerativeModel(model_name=model)
^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'google.generativeai' has no attribute 'GenerativeModel
```
### Expected behavior
A poor result will be random text generated which is not relevant to the prompt provided. An excellent result will be text generated relevant to the template provided.
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https://api.github.com/repos/langchain-ai/langchain/issues/14710 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14710/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14710/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14710/events | https://github.com/langchain-ai/langchain/issues/14710 | 2,041,522,040 | I_kwDOIPDwls55ryd4 | 14,710 | Raise "Message of 'system' type not supported by Gemini" exception by ChatGoogleGenerativeAI with ConversationalRetrievalChain | {
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] | open | false | null | [] | null | 7 | 2023-12-14T11:41:27 | 2023-12-24T07:52:19 | null | NONE | null | ### System Info
langchain==0.0.350
langchain-community==0.0.3
langchain-core==0.1.0
langchain-google-genai==0.0.3
google-ai-generativelanguage==0.4.0
google-api-core==2.15.0
google-auth==2.25.2
google-generativeai==0.3.1
googleapis-common-protos==1.62.0
### 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
```
class LangChainChatService(ChatBaseService):
def __init__(self, model: LangchainChatModel, tool_query=None):
super().__init__()
self.model = model
self.request_manager = api_request_manager_var.get()
self.tool_query = tool_query
async def http_chat_async(self) -> dict:
formatted_chat_history = [] if self.tool_query else self.get_formatted_chat_history(self.model.chat_history)
qa = self._get_qa_chain()
qa.return_source_documents = self.model.return_source_documents
qa.return_generated_question = True
query_start = time.time()
question = self.tool_query or self.model.query
qa_response = await qa.ainvoke({"question": question, "chat_history": formatted_chat_history})
query_end = time.time()
result = {
'query_result': qa_response.get("answer"),
'query_time': int((query_end - query_start) * 1000),
'generated_question': qa_response.get('generated_question'),
'source_documents': [document.__dict__ for document in qa_response.get("source_documents", [])],
}
self._publish_chat_history(result)
return result
def _get_qa_chain(self, callbacks: Callbacks = None) -> BaseConversationalRetrievalChain:
collection_name = get_langchain_collection_name(self.model.client_id)
connection_args = {"host": AppConfig.vector_db_host, "port": AppConfig.vector_db_port}
embeddings = LLMSelector(self.model).get_embeddings()
vector_store = Milvus(embeddings, collection_name=collection_name, connection_args=connection_args)
expression = get_expression_to_fetch_db_text_from_ids(**self.model.model_dump())
# Instance of ChatGoogleGenerativeAI
qa_llm = LLMSelector(self.model).get_language_model(streaming=self.model.stream_response, callbacks=callbacks)
condense_question_llm = LLMSelector(self.model).get_language_model()
qa = ConversationalRetrievalChain.from_llm(
llm=qa_llm,
retriever=vector_store.as_retriever(search_type="similarity", search_kwargs={"k": self.model.similarity_top_k, 'expr': expression}),
condense_question_llm=condense_question_llm,
verbose=True
)
return qa
```
The code use this prompt from langchain code base
```
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Helpful Answer:"""
QA_PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
```
I see that after fetching context document from vector database langchain generate `SystemMessage` along with `HumanMessage` using the above prompt. And there is a validation in `ChatGoogleGenerativeAI` to prevent system message.
This is the possible reason for producing the following error.
`langchain_google_genai.chat_models.ChatGoogleGenerativeAIError: Message of 'system' type not supported by Gemini. Please only provide it with Human or AI (user/assistant) messages.`
### Expected behavior
As I didn't provide any SystemMessage, ChatGoogleGenerativeAI should work without exception.
This bug is related to Langchain. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14709 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14709/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14709/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14709/events | https://github.com/langchain-ai/langchain/issues/14709 | 2,041,427,315 | I_kwDOIPDwls55rbVz | 14,709 | CallbackHandler on_llm_new_token method not fire with ChatGoogleGenerativeAI(Gemini) but works fine with ChatOpenAI when streaming true | {
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] | open | false | null | [] | null | 6 | 2023-12-14T10:43:18 | 2023-12-31T14:35:17 | null | NONE | null | ### System Info
aiohttp==3.9.1
aiosignal==1.3.1
annotated-types==0.6.0
anyio==4.1.0
attrs==23.1.0
cachetools==5.3.2
certifi==2023.11.17
charset-normalizer==3.3.2
colorama==0.4.6
dataclasses-json==0.6.3
frozenlist==1.4.0
google-ai-generativelanguage==0.4.0
google-api-core==2.15.0
google-auth==2.25.2
google-generativeai==0.3.1
googleapis-common-protos==1.62.0
greenlet==3.0.2
grpcio==1.60.0
grpcio-status==1.60.0
idna==3.6
jsonpatch==1.33
jsonpointer==2.4
langchain-community==0.0.3
langchain-core==0.1.0
langchain-google-genai==0.0.3
langsmith==0.0.70
marshmallow==3.20.1
multidict==6.0.4
mypy-extensions==1.0.0
numpy==1.26.2
packaging==23.2
Pillow==10.1.0
proto-plus==1.23.0
protobuf==4.25.1
pyasn1==0.5.1
pyasn1-modules==0.3.0
pydantic==2.5.2
pydantic_core==2.14.5
PyYAML==6.0.1
requests==2.31.0
rsa==4.9
sniffio==1.3.0
SQLAlchemy==2.0.23
tenacity==8.2.3
tqdm==4.66.1
typing-inspect==0.9.0
typing_extensions==4.9.0
urllib3==2.1.0
yarl==1.9.4
### 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
```
from typing import Any, Optional
from uuid import UUID
from langchain.chains import LLMChain
from langchain_community.chat_models import ChatOpenAI
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult
from langchain_core.prompts import PromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
prompt_template = "Tell me details about the Company {name} with 2 bullet point?"
class StreamingLLMCallbackHandler(BaseCallbackHandler):
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
print(token, end='')
def on_llm_end(self, response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) -> Any:
print('\n\n---------------------LLM_END--------------------------')
print(response.generations[0][0].text)
def gemini_stream_with_cb():
llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key='AIzaSPVggdfgerweselzbVCHH', streaming=True, callbacks=[StreamingLLMCallbackHandler()])
llm_chain = LLMChain(llm=llm, prompt=PromptTemplate.from_template(prompt_template), verbose=True)
llm_chain.run(name="Google")
def openai_stream_with_cb():
llm = ChatOpenAI(openai_api_key='sk-pSHx9qACbzdgsdgyuuyNCXpZ0', model='gpt-4', streaming=True, callbacks=[StreamingLLMCallbackHandler()])
llm_chain = LLMChain(llm=llm, prompt=PromptTemplate.from_template(prompt_template), verbose=True)
llm_chain.run(name="Open AI")
if __name__ == '__main__':
gemini_stream_with_cb()
# openai_stream_with_cb()
```
**ChatGoogleGenerativeAI:** on_llm_new_token not triggered, but on_llm_end triggered
**ChatOpenAI**: Both triggered
### Expected behavior
**on_llm_new_token should trigger for ChatGoogleGenerativeAI** | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14708 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14708/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14708/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14708/events | https://github.com/langchain-ai/langchain/pull/14708 | 2,041,369,280 | PR_kwDOIPDwls5h_Afg | 14,708 | Make `doctran` synchronous | {
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] | closed | false | null | [] | null | 1 | 2023-12-14T10:10:55 | 2023-12-28T11:39:55 | 2023-12-28T11:39:55 | CONTRIBUTOR | null | ### Description
I found that the methods in [the doctran library](https://github.com/psychic-api/doctran) have been restructured into [synchronized versions](https://github.com/psychic-api/doctran/commit/14944a59f7513b51e629a850e0de5ae276eabed9),
And [the example ipynb](https://github.com/psychic-api/doctran/blob/main/examples.ipynb) also shows that the code is synchronized, but the README has not been updated yet.
so we need to modify the code and update the documentation.
### Issue
https://github.com/langchain-ai/langchain/issues/14645 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14706 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14706/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14706/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14706/events | https://github.com/langchain-ai/langchain/issues/14706 | 2,041,277,150 | I_kwDOIPDwls55q2re | 14,706 | Issue: what are all dependencies need to install for fetching confluence attachments | {
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] | open | false | null | [] | null | 1 | 2023-12-14T09:19:24 | 2023-12-14T09:26:04 | null | NONE | null | ### Issue you'd like to raise.
below is my dependencies installed till now
django==4.0
django-rest-framework
langchain==0.0.349
pdf2image
chromadb
unstructured
openai
pypdf
tiktoken
django-cors-headers
django-environ
pytesseract==0.3.10
beautifulsoup4==4.12.2
atlassian-python-api==3.38.0
tiktoken==0.4.0
lxml==4.9.2
what else dependencies need to install for fetching confluence attachments.
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/14705 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14705/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14705/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14705/events | https://github.com/langchain-ai/langchain/issues/14705 | 2,041,252,793 | I_kwDOIPDwls55qwu5 | 14,705 | Splitting the document based on headers using Recursive Character Text Splitter | {
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] | closed | false | null | [] | null | 1 | 2023-12-14T09:05:08 | 2023-12-22T09:57:43 | 2023-12-22T09:57:43 | NONE | null | Hi @dossubot.
I have a document where it contains 3 different titles of a single document, i want to create chunk size based on every header, so below is my existing code, pls modify that. and send me accordingly.
def load_docs_only(directory_path):
docs, _, _, _ = load_documents(directory_path)
return docs
def text_splitter_by_char():
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=100,
length_function = len,
return splitter
# Load the content
docs = load_docs_only(cfg.directory_path)
# Split the content
splitter = text_splitter_by_char()
chunks = splitter.split_documents(docs)
chunks | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14704 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14704/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14704/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14704/events | https://github.com/langchain-ai/langchain/pull/14704 | 2,041,243,136 | PR_kwDOIPDwls5h-lGH | 14,704 | Update `google_generative_ai.ipynb` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14703 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14703/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14703/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14703/events | https://github.com/langchain-ai/langchain/issues/14703 | 2,041,224,728 | I_kwDOIPDwls55qp4Y | 14,703 | Issue: Rephrased question in included in the on_llm_new_token method while streaming the response from the LLM | {
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] | open | false | null | [] | null | 6 | 2023-12-14T08:47:45 | 2023-12-26T09:42:51 | null | NONE | null | ### Issue you'd like to raise.
Hi, I am trying to stream the response from the llm back to the client by using a callback with a custom StreamHandler, but the on_llm_new_token also includes the output from the rephrase_question step. while the final response does not include the rephrased answer. I don't want the rephrased question to be present in the response that is streaming.
the StreamHandler class is given below
`class StreamHandler(BaseCallbackHandler):
def __init__(self):
self.text = ""
def on_llm_new_token(self, token: str, **kwargs):
old_text = self.text
self.text += token
# Calculate the new content since the last emission
new_content = self.text[len(old_text) :]
socketio.emit("update_response", {"response": new_content})`
The qa-chain is defined as below:
qa_chain = ConversationalRetrievalChain.from_llm(
llm=chat,
retriever=MyVectorStoreRetriever(
vectorstore=vectordb,
search_type="similarity_score_threshold",
search_kwargs={"score_threshold": SIMILARITY_THRESHOLD, "k": 1},
),
return_source_documents=True,
rephrase_question=False)
response = qa_chain(
{
"question": user_input,
"chat_history":chat_history,
},callbacks=[stream_handler] )
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14702 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14702/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14702/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14702/events | https://github.com/langchain-ai/langchain/issues/14702 | 2,041,221,683 | I_kwDOIPDwls55qpIz | 14,702 | Issue: How to set the Chain with valid/empty input | {
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] | open | false | null | [] | null | 1 | 2023-12-14T08:45:49 | 2023-12-14T08:58:00 | null | NONE | null | ### Issue you'd like to raise.
my code:
only_recall_inputs = RunnableParallel({
"question": itemgetter('question'),
"history": ????????,
"docs": itemgetter('question') | retriever,
})
just a simple chain
I want the "history" part to be [] or ''
how to do this?
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/14701 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14701/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14701/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14701/events | https://github.com/langchain-ai/langchain/issues/14701 | 2,041,187,377 | I_kwDOIPDwls55qgwx | 14,701 | The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() | {
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] | open | false | null | [] | null | 2 | 2023-12-14T08:24:32 | 2023-12-14T12:29:31 | null | NONE | null | ### System Info
python 3.10
langchain version：0.0.350
### 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
when use elasticsearchStore add_doccuments The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
langchain 0.0.317 is ok
upgrade langchain 0.0.350 exist error
### Expected behavior
fix it | {
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] | open | false | null | [] | null | 8 | 2023-12-14T07:07:13 | 2023-12-19T18:45:16 | null | NONE | null | ### System Info
LangChain: 0.0.348
langchain-google-genai: 0.0.3
python: 3.11
os: macOS11.6
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
langchain_google_genai.chat_models.ChatGoogleGenerativeAIError: Message of 'system' type not supported by Gemini. Please only provide it with Human or AI (user/assistant) messages.
### Expected behavior
no error | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14699 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14699/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14699/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14699/events | https://github.com/langchain-ai/langchain/issues/14699 | 2,041,031,199 | I_kwDOIPDwls55p6of | 14,699 | sqlalchemy.exc.InvalidRequestError: Table 'langchain_pg_collection' is already defined for this MetaData instance. | {
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] | closed | false | null | [] | null | 15 | 2023-12-14T06:51:40 | 2023-12-27T19:12:07 | 2023-12-14T21:27:31 | NONE | null | ### Issue you'd like to raise.
what is this issue, and how can i resolve it:
```python
os.environ["AZURE_OPENAI_API_KEY"] = AZURE_OPENAI_API_KEY
os.environ["AZURE_OPENAI_ENDPOINT"] = AZURE_OPENAI_ENDPOINT
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
embedding = OpenAIEmbeddings()
COLLECTION_NAME = "network_team_documents"
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get(DB_DRIVER, DB_DRIVER),
host=os.environ.get(DB_HOST, DB_HOST),
port=int(os.environ.get(DB_PORT, DB_PORT)),
database=os.environ.get(DB_DB, DB_DB),
user=os.environ.get(DB_USER, DB_USER),
password=os.environ.get(DB_PASS, DB_PASS),
)
store = PGVector(
collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
embedding_function=embedding,
extend_existing=True,
)
gpt4 = AzureChatOpenAI(
azure_deployment="GPT4",
openai_api_version="2023-05-15",
)
retriever = store.as_retriever(search_type="similarity", search_kwargs={"k": 10})
qa_chain = RetrievalQA.from_chain_type(llm=gpt4,
chain_type="stuff",
retriever=retriever,
return_source_documents=True)
return qa_chain
```
```python
Traceback (most recent call last):
File "/opt/network_tool/chatbot/views.py", line 21, in chat
chat_object = create_session()
File "/opt/network_tool/chatbot/chatbot_functions.py", line 95, in create_session
store = PGVector(
File "/opt/klevernet_venv/lib/python3.10/site-packages/langchain_community/vectorstores/pgvector.py", line 199, in __init__
self.__post_init__()
File "/opt/klevernet_venv/lib/python3.10/site-packages/langchain_community/vectorstores/pgvector.py", line 207, in __post_init__
EmbeddingStore, CollectionStore = _get_embedding_collection_store()
File "/opt/klevernet_venv/lib/python3.10/site-packages/langchain_community/vectorstores/pgvector.py", line 66, in _get_embedding_collection_store
class CollectionStore(BaseModel):
File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_api.py", line 195, in __init__
_as_declarative(reg, cls, dict_)
File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_base.py", line 247, in _as_declarative
return _MapperConfig.setup_mapping(registry, cls, dict_, None, {})
File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_base.py", line 328, in setup_mapping
return _ClassScanMapperConfig(
File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_base.py", line 578, in __init__
self._setup_table(table)
File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_base.py", line 1729, in _setup_table
table_cls(
File "", line 2, in __new__
File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/util/deprecations.py", line 281, in warned
return fn(*args, **kwargs) # type: ignore[no-any-return]
File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/sql/schema.py", line 436, in __new__
return cls._new(*args, **kw)
File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/sql/schema.py", line 468, in _new
raise exc.InvalidRequestError(
sqlalchemy.exc.InvalidRequestError: Table 'langchain_pg_collection' is already defined for this MetaData instance. Specify 'extend_existing=True' to redefine options and columns on an existing Table object.
```
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14697 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14697/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14697/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14697/events | https://github.com/langchain-ai/langchain/issues/14697 | 2,041,006,966 | I_kwDOIPDwls55p0t2 | 14,697 | SQLagent always giving me AttributeError: items for Snowflake tables | {
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] | open | false | null | [] | null | 8 | 2023-12-14T06:29:48 | 2023-12-14T14:18:57 | null | NONE | null | ### System Info
Langchain version = 0.0.344
Python version = 3.11.5
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here is my code for connecting to Snowflake database and getting the tables and executing them in langchain SQLagent.. Howver i always get AttributeError: items.
from snowflake.sqlalchemy import URL
llm=AzureChatOpenAI(temperature=0.0,deployment_name=gpt-4-32k)
snowflake_url = URL(
account='xxxxx,
user='xxxxxx',
password='xxxxxx',
database='xxxxx',
schema='xxxxxx',
warehouse='xxxxxxx'
)
db = SQLDatabase.from_uri(snowflake_url, sample_rows_in_table_info=1, include_tables=['gc'])
# Create the SQLDatabaseToolkit
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True,
prefix=MSSQL_AGENT_PREFIX,
format_instructions = MSSQL_AGENT_FORMAT_INSTRUCTIONS,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
top_k=30,
early_stopping_method="generate",
handle_parsing_errors = True,
)
question = "List top 10 records from gc"
response = agent_executor.run(question)
Entering new AgentExecutor chain...
Action: sql_db_list_tables
Action Input: ""
Observation: gc
Thought:The 'gc' table is available in the database. I should now check the schema of the 'gc' table to understand its structure and the data it contains.
Action: sql_db_schema
Action Input: "gc"
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File [c:\Anaconda_3\Lib\site-packages\sqlalchemy\sql\base.py:1150](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/sql/base.py:1150), in ColumnCollection.__getattr__(self, key)
[1149](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/sql/base.py:1149) try:
-> [1150](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/sql/base.py:1150) return self._index[key]
[1151](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/sql/base.py:1151) except KeyError as err:
KeyError: 'items'
The above exception was the direct cause of the following exception:
AttributeError Traceback (most recent call last)
Cell In[66], [line 33](vscode-notebook-cell:?execution_count=66&line=33)
[31](vscode-notebook-cell:?execution_count=66&line=31) from langchain.globals import set_debug
[32](vscode-notebook-cell:?execution_count=66&line=32) set_debug(False)
---> [33](vscode-notebook-cell:?execution_count=66&line=33) response = agent_executor.run(question)
File [~\AppData\Roaming\Python\Python311\site-packages\langchain\chains\base.py:507](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:507), in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
[505](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:505) if len(args) != 1:
[506](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:506) raise ValueError("`run` supports only one positional argument.")
--> [507](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:507) return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
[508](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:508) _output_key
[509](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:509) ]
[511](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:511) if kwargs and not args:
[512](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:512) return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
...
[201](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/util/compat.py:201) # https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/
[202](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/util/compat.py:202) # as the __traceback__ object creates a cycle
[203](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/util/compat.py:203) del exception, replace_context, from_, with_traceback
AttributeError: items
### Expected behavior
Should have executed the question. | {
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] | open | false | null | [] | null | 10 | 2023-12-14T06:18:44 | 2023-12-21T20:04:45 | null | NONE | null | ### System Info
**Environment Details**
**Langchain version 0.0.336
Python 3.9.2rc1**
**Error encountered while executing the sample code mentioned in the "Semi_structured_multi_modal_RAG_LLaMA2.ipynb" notebook from the cookbook.**
File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\unstructured\file_utils\filetype.py:551](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured/file_utils/filetype.py:551), in add_metadata_with_filetype.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
[549](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured/file_utils/filetype.py:549) @functools.wraps(func)
...
--> [482](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured_inference/inference/layout.py:482) model = get_model(model_name, **kwargs)
[483](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured_inference/inference/layout.py:483) if isinstance(model, UnstructuredObjectDetectionModel):
[484](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured_inference/inference/layout.py:484) detection_model = model
**TypeError: get_model() got an unexpected keyword argument 'ocr_languages'**
I would appreciate any assistance in resolving this issue. Thank you.
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from typing import Any
from pydantic import BaseModel
from unstructured.partition.pdf import partition_pdf
# Get elements
raw_pdf_elements = partition_pdf(
filename=path + "Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf",# Unstructured first finds embedded image blocks
infer_table_structure=True,
# Post processing to aggregate text once we have the title
max_characters=4000,
new_after_n_chars=3800,
combine_text_under_n_chars=2000,
image_output_dir_path=path,
languages=['eng'],
)
### Expected behavior
The notebook should run without any issues and produce the expected output as documented in the cookbook | {
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] | open | false | null | [] | null | 1 | 2023-12-14T04:54:06 | 2023-12-14T05:01:21 | null | NONE | null | ### System Info
### **SYSTEM INFO**
LangChain version : 0.0.345
Python version : 3.9.6
### **ISSUE**
I create this custom function which will throw an error if the vectorestore cannot retrieve any relevant document
```
def check_threshold(inp, vecs):
query = inp['question']
threshold = inp['threshold']
d = [doc for doc,score in vecs.similarity_search_with_relevance_scores(query) if score >= threshold]
if len(d) < 1:
raise Exception("Not found!")
return "\n\n".join([x.page_content for x in d])
```
I want to use another chain if the main chain fails by using `with_fallbacks` function in the main chain
```
main_chain = ({
"context" : lambda x: check_threshold(x, vecs), "question" : lambda x: x['question']
} | prompt | llm | StrOutputParser()
).with_fallbacks([fallback_chain])
```
In the above code, the fallback_chain never gets triggered.
**PS : The above code is just an example, the original code uses more complicated calculations with many exceptions raise in several custom functions. Therefore, It is not feasible to use built-in Python try-except error handler**
### 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
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```
def check_threshold(inp, vecs):
query = inp['question']
threshold = inp['threshold']
d = [doc for doc,score in vecs.similarity_search_with_relevance_scores(query) if score >= threshold]
if len(d) < 1:
raise Exception("Not found!")
return "\n\n".join([x.page_content for x in d])
main_chain = ({
"context" : lambda x: check_threshold(x, vecs), "question" : lambda x: x['question']
} | prompt | llm | StrOutputParser()
).with_fallbacks([fallback_chain])
main_chain.invoke({"question":"Hello, good morning"})
```
### Expected behavior
fallback_chain get triggered whenever the main_chain raise an exception | {
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] | open | false | null | [] | null | 1 | 2023-12-14T03:49:26 | 2023-12-14T03:51:43 | null | CONTRIBUTOR | null | ### Issue you'd like to raise.
Recently, langchain released the google gemini independent package to connect the google gemini LLM capabilities.
This is also the first independent package released by langchain, which is a very big progress and change.
But I noticed that the name of this package is langchain-google-genai, which may not be very systematic.
Perhaps, we can use python's namespace feature to manage all langchain-related packages.
Documentation about namespace package:
- https://packaging.python.org/guides/packaging-namespace-packages/
- https://www.python.org/dev/peps/pep-0420/
### Suggestion:
Use the namespace capability to manage and publish all independent packages of langchain. The specific directory structure is as follows：
docs: https://packaging.python.org/en/latest/guides/packaging-namespace-packages/
```
pyproject.toml # AND/OR setup.py, setup.cfg
src/
langchain/ # namespace package
# No __init__.py here.
google-genai/
# Regular import packages have an __init__.py.
__init__.py
module.py
```
and then, you can use like:
```python
import langchain.google-genai
from langchain import google-genai
# ... code
``` | {
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] | closed | false | null | [] | null | 3 | 2023-12-14T03:36:38 | 2024-01-02T19:39:51 | 2024-01-01T22:37:36 | CONTRIBUTOR | null | Description: Volcano Ark is an enterprise-grade large-model service platform for developers, providing a full range of functions and services such as model training, inference, evaluation, fine-tuning. You can visit its homepage at https://www.volcengine.com/docs/82379/1099455 for details. This change could help developers use the platform for embedding.
Issue: None
Dependencies: volcengine
Tag maintainer: @baskaryan
Twitter handle: @hinnnnnnnnnnnns | {
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- [X] Update README
- [X] Update docs
- [X] Bump version | {
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This agent writes Python code that is formatted and linted using `black`, `ruff`, and `mypy`, but does not execute the code. It writes the code to a temporary file and then runs the linters. Once these checks pass, the code is returned.
# Dependencies
- black
- ruff
- mypy
# Demo
The functionality can be seen here: https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo
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