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Further Enhancement to WeaviateHybridSearchRetriever
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[] | null | 2 | 2023-09-20T04:19:16 | 2023-09-27T22:59:49 | null |
CONTRIBUTOR
| null |
**Description:**
Upon working with the retriever further, I realized that when it is used in an existing chain, you don’t directly call `get_relevant_documents()`. This makes it challenging to pass the `hybrid_search_kwargs` to the underly function.
To address this, I’ve introduced a minor patch in this commit. By adding `hybrid_search_kwargs` as a class variable, we can now handle a wider range of realistic use cases without conflicting with the previously pushed version.
Here is a simple example:
```python
retreiver = WeaviateHybridSearchRetriever(
client=client,
index_name="Document",
text_key="content",
hybrid_search_kwargs={"properties": ["title", "summary", "content"]},
)
llm = OpenAI()
conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm,retriever=retreiver)
# When called within conversation_chain invocation
def _get_relevant_documents(self, ...):
...
# Prioritizes function argument, then class var, finally empty {}
if hybrid_search_kwargs is None:
hybrid_search_kwargs = self.hybrid_search_kwargs or {}
...
```
**Tag maintainer:**
@hwchase17
|
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PR_kwDOIPDwls5avIQN
| 10,816 |
corrected spelling (#62)
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[] | null | 1 | 2023-09-20T04:12:41 | 2023-09-20T04:41:49 | 2023-09-20T04:41:49 |
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PR_kwDOIPDwls5avGrG
| 10,815 |
Update search.py
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[] | null | 1 | 2023-09-20T04:08:19 | 2023-09-20T04:42:47 | null |
CONTRIBUTOR
| null |
changed return type from string to List
continuation of the closed PR
https://github.com/langchain-ai/langchain/pull/10743#discussion_r1330806902
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I_kwDOIPDwls5xfcys
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SeleniumURLLoader load asynchronous js, need wait
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[] | null | 2 | 2023-09-20T04:00:24 | 2023-11-01T08:49:10 | null |
NONE
| null |
### System Info
Mac OS ventura 13.4.1
### 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
langchain/document_loaders/url_selenium.py
def load(self) -> List[Document]:
"""Load the specified URLs using Selenium and create Document instances.
Returns:
List[Document]: A list of Document instances with loaded content.
"""
from unstructured.partition.html import partition_html
docs: List[Document] = list()
driver = self._get_driver()
for url in self.urls:
try:
driver.get(url)
sleep(30) // need wait
page_content = driver.page_source
elements = partition_html(text=page_content)
text = "\n\n".join([str(el) for el in elements])
metadata = {"source": url}
docs.append(Document(page_content=text, metadata=metadata))
except Exception as e:
if self.continue_on_failure:
logger.error(f"Error fetching or processing {url}, exception: {e}")
else:
raise e
driver.quit()
return docs
### Expected behavior
timer is config parms, user can use
SeleniumURLLoader({waittime: 30})
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PR_kwDOIPDwls5au4Jl
| 10,813 |
Update base.py
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NONE
| null |
as per definition, `include_df_in_prompt` should either be true or false
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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| 1,904,013,281 |
PR_kwDOIPDwls5au3-R
| 10,812 |
set litellm max_tokens=None
|
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open
| false | null |
[] | null | 5 | 2023-09-20T03:03:34 | 2023-10-03T14:05:31 | null |
NONE
| null |
- **Description:** The default value of the `max_tokens` for litellm is 256. It should be increased to avoid truncation of replies.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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| 1,903,925,576 |
I_kwDOIPDwls5xe5lI
| 10,811 |
SQLAgent fails to connect to Postgres multiple schemas
|
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[] | null | 3 | 2023-09-20T01:19:04 | 2023-12-27T16:04:48 | 2023-12-27T16:04:48 |
NONE
| null |
### System Info
I'm using latest langchain version 0.0.295. It turns out langchain SQLAgent always connects to Postgres public schema. I need SQLAgent to check all schemas to generate SQL query. How to configure SQLAgent to connect to all Postgres schemas?
Tried both pg_uri = f"postgresql+psycopg2://{username}:{password}@{host}:{port}/{mydatabase}?currentSchema=myschema", pg_uri = f"postgresql+psycopg2://{username}:{password}@{host}:{port}/{mydatabase}?search_path=myschema" when creating SQLDatabase: db = SQLDatabase.from_uri(pg_uri). It doesn't work.
### 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 creating SQLDatabase for postgres, I can't specify schema. It only connects to postgres public schema. As a result, SQLAgent doesn't produce meaningful result.
# pg_uri = f"postgresql+psycopg2://{username}:{password}@{host}:{port}/{mydatabase}?search_path=myschema"
pg_uri = f"postgresql+psycopg2://{username}:{password}@{host}:{port}/{mydatabase}?currentSchema=myschema"
db = SQLDatabase.from_uri(pg_uri)
### Expected behavior
Expect SQLAgent or SQLDatabase can configure schemas for Postgres so that SQLAgent can generate meaningful SQL query.
|
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I_kwDOIPDwls5xexFK
| 10,810 |
INTERMITTENT ERROR: AzureCogsImageAnalysisTool._run() got an unexpected keyword argument 'url'
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[] | null | 3 | 2023-09-20T00:25:58 | 2023-12-27T16:04:53 | 2023-12-27T16:04:52 |
NONE
| null |
### System Info
Python 3.11.0
LangChain==0.0.295
Azure-ai-vision==0.15.1b1
Streamlit
### Who can help?
@agola11 @hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Important to note that the below error is INTERMITTEND using the exact same input every single time. Sometimes it works for an hour without error, then it will throw the below error a few times and return to normal. Other times it throws this error like 20 times before it will return to normal and start working again. It is like it gets confused about what inputs the agent expect at random.
Also note that this is a simplified version. The reason I use create_conversational_retrieval_agent is because I pass multiple tools like O365, SQLDatabase ect. But I can still produce the error without them.
**Error:**
TypeError: AzureCogsImageAnalysisTool._run() got an unexpected keyword argument 'url'
Traceback:
File "C:\_Dev\AZURE-AGENT-TEST\venv\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "C:\_Dev\AZURE-AGENT-TEST\home.py", line 409, in <module>
main()
File "C:\_Dev\AZURE-AGENT-TEST\home.py", line 256, in main
handle_userinput(user_question, image_url)
File "C:\_Dev\AZURE-AGENT-TEST\home.py", line 151, in handle_userinput
response = st.session_state.conversation({'input': user_question + " " + '"' + file_url + '"' })
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\_Dev\AZURE-AGENT-TEST\venv\Lib\site-packages\langchain\chains\base.py", line 292, in __call__
raise e
File "C:\_Dev\AZURE-AGENT-TEST\venv\Lib\site-packages\langchain\chains\base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\_Dev\AZURE-AGENT-TEST\venv\Lib\site-packages\langchain\agents\agent.py", line 1122, in _call
next_step_output = self._take_next_step(
^^^^^^^^^^^^^^^^^^^^^
File "C:\_Dev\AZURE-AGENT-TEST\venv\Lib\site-packages\langchain\agents\agent.py", line 977, in _take_next_step
observation = tool.run(
^^^^^^^^^
File "C:\_Dev\AZURE-AGENT-TEST\venv\Lib\site-packages\langchain\tools\base.py", line 356, in run
raise e
File "C:\_Dev\AZURE-AGENT-TEST\venv\Lib\site-packages\langchain\tools\base.py", line 328, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
**Initialize the agent:**
toolkit = AzureCognitiveServicesToolkit()
tools = toolkit.get_tools()
llm = ChatOpenAI(temperature=0.7)
st.session_state.conversation = create_conversational_retrieval_agent(llm,
tools,
verbose=False,
max_iterations=3,
early_stopping_method='generate',
AgentType=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION)
**Then handle the user input:**
user_question = "What can I make with these ingredients?"
file_url = "https://images.openai.com/blob/9ad5a2ab-041f-475f-ad6a-b51899c50182/ingredients.png"
response = st.session_state.conversation({'input': user_question + " " + '"' + file_url + '"' })
st.write(response)
### Expected behavior
I expect it to not throw this error intermittently.
|
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| 1,903,867,154 |
I_kwDOIPDwls5xerUS
| 10,809 |
OpenAI class missing `predict` method as described in the Quickstart guide
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[] | null | 1 | 2023-09-19T23:51:29 | 2023-09-20T00:00:51 | 2023-09-20T00:00:51 |
NONE
| null |
### Issue with current documentation:
While following the Quickstart guide in the LangChain documentation, I encountered an AttributeError when trying to use the `predict` method with an instance of the OpenAI class. It seems like the `predict` method is not available in the OpenAI class as described in the guide.
I'm running the following code from the Langchain Quickstart Guide, LLMs section:
```
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
llm = OpenAI()
chat_model = ChatOpenAI()
llm.predict("hi!")
>>> "Hi"
chat_model.predict("hi!")
>>> "Hi"
```
Below are the steps I took before running:
1. Installed LangChain using `pip install langchain`.
2. Installed OpenAI Python package using `pip install openai`.
3. Set up the OpenAI API key as described in the guide.
4. Created an instance of the OpenAI class and attempted to call the `predict` method with a string argument, as shown in the guide.
**Expected behavior:** Based on the Quickstart guide, I expected to be able to call the `predict` method with a string argument and receive a string as the output.
**Actual behavior:** When attempting to call the `predict` method, I received the following error message:
AttributeError: 'OpenAI' object has no attribute 'predict'
**Environment:**
- LangChain version: (0.0.112)
- OpenAI Python package version: (0.27.2)
- Python version: 3.9.5
- Operating System: macOS
I checked the available methods of the OpenAI class using the `dir()` function and confirmed that the `predict` method is not available. I also tried passing a list of strings to the `generate` method as an alternative, based on the methods that are available, but this did not produce the expected result.
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5auW7K
| 10,808 |
misc: add gpt-3.5-turbo-instruct to model_token_mapping
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CONTRIBUTOR
| null |
A one-line fix to get`max_tokens=-1` working `OpenAI` class for `gpt-3.5-turbo-instruct` model.
Closes https://github.com/langchain-ai/langchain/issues/10806
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PR_kwDOIPDwls5auW66
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Harrison/add vald
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I_kwDOIPDwls5xenpf
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Error when using gpt-3.5-turbo-instruct: model_token_mapping is missing an entry for gpt-3.5-turbo-instruct
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[] | null | 1 | 2023-09-19T23:26:18 | 2023-09-20T00:03:17 | 2023-09-20T00:03:17 |
CONTRIBUTOR
| null |
### System Info
LangChain version: 0.0.295 (just upgraded to this version to use gpt-3.5-turbo-instruct)
### Who can help?
@hwchase17 @agola
### 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
Minimal code to reproduce:
```python
# load OpenAI API Key
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.1, model_name="gpt-3.5-turbo-instruct", max_tokens=-1)
llm("give me a list of Chinese dishes and their recipes")
```
Error message:
>```ValueError: Unknown model: gpt-3.5-turbo-instruct. Please provide a valid OpenAI model name.Known models are: gpt-4, gpt-4-0314, gpt-4-0613, gpt-4-32k, gpt-4-32k-0314, gpt-4-32k-0613, gpt-3.5-turbo, gpt-3.5-turbo-0301, gpt-3.5-turbo-0613, gpt-3.5-turbo-16k, gpt-3.5-turbo-16k-0613, text-ada-001, ada, text-babbage-001, babbage, text-curie-001, curie, davinci, text-davinci-003, text-davinci-002, code-davinci-002, code-davinci-001, code-cushman-002, code-cushman-001```
Cause of the error: looks like it's because the `model_token_mapping` is missing an entry for `gpt-3.5-turbo-instruct`: https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/openai.py#L555
### Expected behavior
The code succeeds without error
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PR_kwDOIPDwls5auVlZ
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Revert "Add ChatGLM for llm and chat_model by using ChatGLM API (#9797)"
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COLLABORATOR
| null |
@etveritas reverting for now until this is resolved https://github.com/langchain-ai/langchain/pull/9797/files#r1330795585, apologies for merging too eagerly!
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improve notebook
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Introducing Enhanced Functionality to WeaviateHybridSearchRetriever: Accepting Additional Keyword Arguments
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[] | null | 2 | 2023-09-19T21:38:24 | 2023-09-19T23:08:01 | 2023-09-19T22:56:22 |
CONTRIBUTOR
| null |
**Description:**
This commit enriches the `WeaviateHybridSearchRetriever` class by introducing a new parameter, `hybrid_search_kwargs`, within the `_get_relevant_documents` method. This parameter accommodates arbitrary keyword arguments (`**kwargs`) which can be channeled to the inherited public method, `get_relevant_documents`, originating from the `BaseRetriever` class.
This modification facilitates more intricate querying capabilities, allowing users to convey supplementary arguments to the `.with_hybrid()` method. This expansion not only makes it possible to perform a more nuanced search targeting specific properties but also grants the ability to boost the weight of searched properties, to carry out a search with a custom vector, and to apply the Fusion ranking method. The documentation has been updated accordingly to delineate these new possibilities in detail.
In light of the layered approach in which this search operates, initiating with `query.get()` and then transitioning to `.with_hybrid()`, several advantageous opportunities are unlocked for the hybrid component that were previously unattainable.
Here’s a representative example showcasing a query structure that was formerly unfeasible:
[Specific Properties Only](https://weaviate.io/developers/weaviate/search/hybrid#selected-properties-only)
"The example below illustrates a BM25 search targeting the keyword 'food' exclusively within the 'question' property, integrated with vector search results corresponding to 'food'."
```python
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_hybrid(
query="food",
properties=["question"], # Will now be possible moving forward
alpha=0.25
)
.with_limit(3)
.do()
)
```
This functionality is now accessible through my alterations, by conveying `hybrid_search_kwargs={"properties": ["question", "answer"]}` as an argument to `WeaviateHybridSearchRetriever.get_relevant_documents()`. For example:
```python
import os
from weaviate import Client
from langchain.retrievers import WeaviateHybridSearchRetriever
client = Client(
url=os.getenv("WEAVIATE_CLIENT_URL"),
additional_headers={
"X-OpenAI-Api-Key": os.getenv("OPENAI_API_KEY"),
"Authorization": f"Bearer {os.getenv('WEAVIATE_API_KEY')}",
},
)
index_name = "Document"
text_key = "content"
attributes = ["title", "summary", "header", "url"]
retriever = ExtendedWeaviateHybridSearchRetriever(
client=client,
index_name=index_name,
text_key=text_key,
attributes=attributes,
)
# Warning: to utilize properties in this way, each use property must also be in the list `attributes + [text_key]`.
hybrid_search_kwargs = {"properties": ["summary^2", "content"]}
query_text = "Some Query Text"
relevant_docs = retriever.get_relevant_documents(
query=query_text,
hybrid_search_kwargs=hybrid_search_kwargs
)
```
In my experience working with the `weaviate-client` library, I have found that these supplementary options stand as vital tools for refining/finetuning searches, notably within multifaceted datasets. As a final note, this implementation supports both backwards and forward (within reason) compatiblity. It accommodates any future additional parameters Weaviate may add to `.with_hybrid()`, without necessitating further alterations.
**Additional Documentation:**
For a more comprehensive understanding and to explore a myriad of useful options that are now accessible, please refer to the Weaviate documentation:
- [Fusion Ranking Method](https://weaviate.io/developers/weaviate/search/hybrid#fusion-ranking-method)
- [Selected Properties Only](https://weaviate.io/developers/weaviate/search/hybrid#selected-properties-only)
- [Weight Boost Searched Properties](https://weaviate.io/developers/weaviate/search/hybrid#weight-boost-searched-properties)
- [With a Custom Vector](https://weaviate.io/developers/weaviate/search/hybrid#with-a-custom-vector)
**Tag Maintainer:**
@hwchase17 - I have tagged you based on your frequent contributions to the pertinent file, `/retrievers/weaviate_hybrid_search.py`. My apologies if this was not the appropriate choice.
Thank you for considering my contribution, I look forward to your feedback, and to future collaboration.
|
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| 1,903,726,835 |
I_kwDOIPDwls5xeJDz
| 10,801 |
GrobidParser: process_xml chunk_bboxes[0][0]["page"], IndexError: list index out of range
|
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[] | null | 2 | 2023-09-19T21:09:13 | 2023-12-26T16:04:41 | 2023-12-26T16:04:41 |
NONE
| null |
Using the GrobidParser with `segment_sentences=False` results in `langchain/document_loaders/parsers/grobid.py", line 87, in process_xml
chunk_bboxes[0][0]["page"],
IndexError: list index out of range` for some PDFs.
From my own testing with the offending PDFs I suspect the following if-block in the GrobidParser is indented incorrectly, it needs one more level of indentation. Adding this indentation solved the issue on my end.
https://github.com/langchain-ai/langchain/blob/20c6ade2fc24c458a080eaa468add7c61dc8dbc3/langchain/document_loaders/parsers/grobid.py#L80-L93
If you need a reproduction to verify, let me know.
|
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| 1,903,697,606 |
PR_kwDOIPDwls5at0On
| 10,800 |
gradient.ai LLM intregration
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[] | null | 8 | 2023-09-19T20:43:34 | 2023-09-24T22:59:08 | 2023-09-21T14:29:16 |
CONTRIBUTOR
| null |
DRAFT
- **Description:** This PR implements a new LLM API to https://gradient.ai
- **Issue:** Feature request for LLM #10745
- **Dependencies**: No additional dependencies are introduced.
- **Tag maintainer:** I am opening this PR for visibility, once ready for review I'll tag.
- ```make format && make lint && make test``` is running.
- added a `integration` and `mock unit` test.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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| 1,903,694,228 |
PR_kwDOIPDwls5atzeK
| 10,799 |
fix qa structured link
|
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[] | null | 1 | 2023-09-19T20:40:42 | 2023-09-19T20:40:50 | 2023-09-19T20:40:48 |
COLLABORATOR
| null |
redirect not working for some reason
|
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| 1,903,665,132 |
I_kwDOIPDwls5xd5_s
| 10,798 |
Query: Is It Possible to Extract Content Using All HTML Tags in ReadTheDocsLoader?
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[] | null | 4 | 2023-09-19T20:15:53 | 2023-12-26T16:04:46 | 2023-12-26T16:04:46 |
NONE
| null |
### Issue you'd like to raise.
According to the [ReadTheDocsLoader documentation](https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.readthedocs.ReadTheDocsLoader.html), this loader iterates through all the files located under a specified path and extracts the actual content of the files by retrieving `main` html tags.
```python
from langchain.document_loaders import ReadTheDocsLoader
loader = ReadTheDocsLoader("docs", encoding="utf-8", features="html.parser")
docs = loader.load()
len(docs)
```
Question: In my HTML files, I don't have main HTML tags. Is it possible to configure the ReadTheDocsLoader to extract content from files by capturing all HTML tags instead of just the main HTML tags? Alternatively, are there alternative loaders available that can traverse all files within a specified path and extract content by capturing all HTML tags?
|
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PR_kwDOIPDwls5atpgo
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make agent action serializable
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[] | null | 1 | 2023-09-19T20:01:57 | 2023-09-19T23:16:16 | 2023-09-19T23:16:15 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5atkS5
| 10,796 |
LLMRails
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[] | null | 2 | 2023-09-19T19:43:47 | 2023-09-20T03:33:33 | 2023-09-20T03:33:33 |
CONTRIBUTOR
| null |
### LLMRails Integration
This PR provides integration with LLMRails. Implemented here are:
langchain/vectorstore/llm_rails.py
tests/integration_tests/vectorstores/test_llm_rails.py
docs/extras/integrations/vectorstores/llm-rails.ipynb
|
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PR_kwDOIPDwls5atWev
| 10,795 |
Batching for hf_pipeline
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[] | null | 2 | 2023-09-19T18:56:05 | 2023-10-25T06:34:55 | 2023-09-25T17:23:12 |
CONTRIBUTOR
| null |
The huggingface pipeline in langchain (used for locally hosted models) does not support batching. If you send in a batch of prompts, it just processes them serially using the base implementation of _generate: https://github.com/docugami/langchain/blob/master/libs/langchain/langchain/llms/base.py#L1004C2-L1004C29
This PR adds support for batching in this pipeline, so that GPUs can be fully saturated. I updated the accompanying notebook to show GPU batch inference.
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PR_kwDOIPDwls5atAJA
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format intermediate steps
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[] | null | 1 | 2023-09-19T17:42:19 | 2023-09-20T20:02:56 | 2023-09-20T20:02:55 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5as3A7
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Update sequential_chains.mdx (#64)
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[] | null | 1 | 2023-09-19T17:14:32 | 2023-09-19T22:56:52 | 2023-09-19T22:56:52 |
CONTRIBUTOR
| null |
Fixed some more grammatical issues
@baskaryan
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Issue: output_parser raises error when using create_pandas_dataframe_agent with GPT4ALL model
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[] | null | 2 | 2023-09-19T17:03:06 | 2023-12-26T16:04:51 | 2023-12-26T16:04:50 |
NONE
| null |
### Issue you'd like to raise.
**I tried to use create_pandas_dataframe_agent with GPT4ALL to query a csv. This is the output:**
```
Enter a query: Analyse the data and give precise answer to the question that is given below. Provide answer only to what
is asked. Answer should be with respect to the whole document. Question: How many employees are male"
> Entering new AgentExecutor chain...
Thought: I will use pandas dataframe to get the summary of the data
Action: I will use pandas dataframe to get the summary of the data
Observation: The summary of the data is given below
... (this Observation/Action/Observation can repeat N times)
Final Answer: The summary of the data shows that there are 4 male employees in the company.Traceback (most recent call last):
File "C:\project\privateGPT\privateGPT\analyticsGPT.py", line 102, in <module>
main()
File "C:\project\privateGPT\privateGPT\analyticsGPT.py", line 73, in main
agent.run(query)
File "C:\Python311\Lib\site-packages\langchain\chains\base.py", line 475, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\langchain\chains\base.py", line 282, in __call__
raise e
File "C:\Python311\Lib\site-packages\langchain\chains\base.py", line 276, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Python311\Lib\site-packages\langchain\agents\agent.py", line 1036, in _call
next_step_output = self._take_next_step(
^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\langchain\agents\agent.py", line 844, in _take_next_step
raise e
File "C:\Python311\Lib\site-packages\langchain\agents\agent.py", line 833, in _take_next_step
output = self.agent.plan(
^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\langchain\agents\agent.py", line 457, in plan
return self.output_parser.parse(full_output)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\langchain\agents\mrkl\output_parser.py", line 61, in parse
raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `
Thought: I will use pandas dataframe to get the summary of the data
Action: I will use pandas dataframe to get the summary of the data`
```
Given below is my code snippet:
```
#!/usr/bin/env python3
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.llms import GPT4All, LlamaCpp
import chromadb
import os
import argparse
import time
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from langchain.agents import create_pandas_dataframe_agent
if not load_dotenv():
print("Could not load .env file or it is empty. Please check if it exists and is readable.")
exit(1)
embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
persist_directory = os.environ.get('PERSIST_DIRECTORY')
model_type = os.environ.get('MODEL_TYPE')
model_path = os.environ.get('MODEL_PATH')
model_n_ctx = os.environ.get('MODEL_N_CTX')
model_n_batch = int(os.environ.get('MODEL_N_BATCH',8))
target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
from constants import CHROMA_SETTINGS
def main():
args = parse_arguments()
callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
if model_type == "LlamaCpp":
llm = LlamaCpp(model_path=model_path, max_tokens=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks, verbose=False)
elif model_type == "GPT4All":
llm = GPT4All(model=model_path, max_tokens=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=False)
else:
raise Exception(f"Model type {model_type} is not supported. Please choose one of the following: LlamaCpp, GPT4All")
df = pd.read_csv('C:/project/privateGPT/privateGPT/source_documents/employees.csv')
agent = create_pandas_dataframe_agent(llm,
df, verbose=True, handle_parsing_errors="Check your output and make sure it conforms!")
while True:
query = input("\nEnter a query: ")
if query == "exit":
break
if query.strip() == "":
continue
# Get the answer from the chain
start = time.time()
agent.run(query)
end = time.time()
def parse_arguments():
parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
'using the power of LLMs.')
parser.add_argument("--hide-source", "-S", action='store_true',
help='Use this flag to disable printing of source documents used for answers.')
parser.add_argument("--mute-stream", "-M",
action='store_true',
help='Use this flag to disable the streaming StdOut callback for LLMs.')
return parser.parse_args()
if __name__ == "__main__":
main()
```
### Suggestion:
_No response_
|
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| 1,903,367,511 |
PR_kwDOIPDwls5ass3u
| 10,791 |
Corrected some spelling mistakes and grammatical errors
|
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[] | null | 1 | 2023-09-19T16:42:28 | 2023-09-19T17:08:59 | 2023-09-19T17:08:59 |
CONTRIBUTOR
| null |
Corrected some spelling mistakes and grammatical errors
CC: @baskaryan, @eyurtsev, @hwchase17.
|
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| 1,903,282,525 |
PR_kwDOIPDwls5asamI
| 10,790 |
add agent output parsers
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[] | null | 2 | 2023-09-19T15:51:34 | 2023-09-20T19:10:11 | 2023-09-20T19:10:09 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5xccaS
| 10,789 |
Prompt was changed to different meaning
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[] | null | 2 | 2023-09-19T15:51:05 | 2023-09-27T13:52:06 | 2023-09-27T13:52:05 |
NONE
| null |
### System Info
I'm building a chatbot to introduce matching job openings from files to users. When the user input "I want to find a job", it is modified to "What kind of job are you looking for?" which ask the model what job he is looking for, but of course, it won't get an answer.
Human input: I want to find a job
Human input in Prompt after formatting: What kind of job are you looking for?
Chatbot: As an AI, I don't have personal preferences or the ability to look for a job.
Here is my prompt and conversation:
```
template = """
You are an AI assistant specifically tasked with finding matching
job opportunities in our job data based on user requests.
###
Context from data: {context}
###
{chat_history}
Human: {question}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["question", "chat_history", "context"],
template=template
)
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=db.as_retriever(),
combine_docs_chain_kwargs={"prompt": prompt},
memory=memory,
verbose=True
)
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
template = """
You are an AI assistant specifically tasked with finding matching
job opportunities in our job data based on user requests.
###
Context from data: {context}
###
{chat_history}
Human: {question}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["question", "chat_history", "context"],
template=template
)
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=db.as_retriever(),
combine_docs_chain_kwargs={"prompt": prompt},
memory=memory,
verbose=True
)
### Expected behavior
the prompt should be what user have input
|
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| 1,903,273,291 |
PR_kwDOIPDwls5asYm0
| 10,788 |
Fix for versioned OpenAI instruct models
|
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[] | null | 2 | 2023-09-19T15:45:50 | 2023-09-19T22:50:07 | 2023-09-19T22:50:06 |
CONTRIBUTOR
| null |
Versioned OpenAI instruct models may end with numbers, e.g. `gpt-3.5-turbo-instruct-0914`.
Fixes https://github.com/langchain-ai/langchainjs/issues/2669 in Python
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| 1,903,231,986 |
I_kwDOIPDwls5xcQPy
| 10,787 |
Document Comparison, OPENAI_FUNCTIONS does not always look into the tools
|
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[] | null | 6 | 2023-09-19T15:22:10 | 2023-12-26T16:04:57 | 2023-12-26T16:04:56 |
NONE
| null |
### System Info
Having basicaly this demo code and the issue is that it's not working reliably!
Sometimes OPENAI_FUNCTIONS just doesn't look into tools and telling that he can't find the documents.
Seems like this happens because OpenAI decides on it's own if he rather use tools or replies on his own knowledge.
I also don't want to fill the memory with all the data every time I am starting a new conversation, this does not work with huge data files.
Any other way to solve this?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from pydantic import BaseModel, Field
from langchain.chat_models import ChatOpenAI
from langchain.agents import Tool
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
class DocumentInput(BaseModel):
question: str = Field()
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
tools = []
files = [
# https://abc.xyz/investor/static/pdf/2023Q1_alphabet_earnings_release.pdf
{
"name": "alphabet-earnings",
"path": "/Users/harrisonchase/Downloads/2023Q1_alphabet_earnings_release.pdf",
},
# https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update
{
"name": "tesla-earnings",
"path": "/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf",
},
]
for file in files:
loader = PyPDFLoader(file["path"])
pages = loader.load_and_split()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(pages)
embeddings = OpenAIEmbeddings()
retriever = FAISS.from_documents(docs, embeddings).as_retriever()
# Wrap retrievers in a Tool
tools.append(
Tool(
args_schema=DocumentInput,
name=file["name"],
description=f"useful when you want to answer questions about {file['name']}",
func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),
)
)
llm = ChatOpenAI(
temperature=0,
model="gpt-3.5-turbo-0613",
)
agent = initialize_agent(
agent=AgentType.OPENAI_FUNCTIONS,
tools=tools,
llm=llm,
verbose=True,
)
agent({"input": "did alphabet or tesla have more revenue?"})
### Expected behavior
Always checking the tools before answering a question.
|
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PR_kwDOIPDwls5asPwX
| 10,786 |
tool rendering
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[] | null | 1 | 2023-09-19T15:22:09 | 2023-09-19T19:05:40 | 2023-09-19T19:05:39 |
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PR_kwDOIPDwls5asK1a
| 10,785 |
Bagatur/bump 295
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I_kwDOIPDwls5xb-kk
| 10,784 |
initializing and using a Supabase vector store using SupabaseVectorStore
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[] | null | 2 | 2023-09-19T14:46:15 | 2023-12-26T16:07:16 | 2023-12-26T16:05:00 |
NONE
| null |
### System Info
`Python 3.11.4`
`langchain==0.0.271`
`lark==1.1.7`
`openai==0.27.8`
### Who can help?
Getting the following error:
```sh
retriever = SelfQueryRetriever.from_llm(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py", line 135, in from_llm
structured_query_translator = _get_builtin_translator(vectorstore)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py", line 45, in _get_builtin_translator
raise ValueError(
ValueError: Self query retriever with Vector Store type <class 'langchain.vectorstores.supabase.SupabaseVectorStore'> not supported.
```
_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
- initialize a SupabaseVectorStore. I want to use existing index, embeddings already created and stored in table `documents`
table definition:
```sql
create table
public.documents (
id bigserial,
content text null,
metadata jsonb null,
embedding public.vector null,
constraint documents_pkey primary key (id)
) tablespace pg_default;
```
```python
document_content_description = "Brief summary of a movie"
embeddings = OpenAIEmbeddings()
vectorstore = SupabaseVectorStore(
client=supabase,
embedding=embeddings,
table_name="documents",
query_name="match_documents",
)
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)
retriever.get_relevant_documents("What are some movies about dinosaurs")
```
### Expected behavior
return relevant documents
|
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| 1,903,062,021 |
I_kwDOIPDwls5xbmwF
| 10,783 |
Message History not working with LLMSingleActionAgent
|
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[] | null | 7 | 2023-09-19T13:59:39 | 2023-09-20T13:14:16 | 2023-09-20T13:04:54 |
NONE
| null |
### System Info
My systeminfo:
langchain 0.0.294
Python 3.11
### Who can help?
Im not sure, maybe @hwchase17 and @agola11 can help
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
That is my code:
```python
openai.api_key = config.openai_api_key
llm = ChatOpenAI(temperature=1, model="gpt-3.5-turbo-16k", max_tokens=1024)
used_tools = []
for tool in tools_to_use:
vector_store = get_vector_store(tool.get("collection"))
index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context)
vector_store_info = VectorStoreInfo(
content_info=tool.get("description"),
metadata_info=[]
)
retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info)
used_tools.append(
Tool(
name=tool.get("name"),
description=tool.get("description"),
func=lambda q: str(retriever.retrieve(q)),
)
)
message_history = PostgresChatMessageHistory(
connection_string=f"postgresql://postgres:{postgres_str}", session_id=session_id, table_name="chat_history",
)
prompt = CustomPromptTemplate(template=get_prompt_template(), tools=used_tools,
input_variables=["input", "intermediate_steps", "chat_history"])
output_parser = CustomOutputParser()
memory = ConversationBufferMemory(
memory_key="chat_history", chat_memory=message_history
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in used_tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nFinal Answer:"], # Observation
allowed_tools=tool_names,
handle_parsing_errors=True,
)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=used_tools, verbose=debug,
memory=memory, return_intermediate_steps=True)
response = agent_executor(user_prompt)
```
### Expected behavior
If i print the params, it shows my chat history (so it's successfully loaded into code and into the agent).
```
{'intermediate_steps': [], 'stop': ['\nFinal Answer:'], 'input': 'What was the last thing I said?', 'chat_history': 'Human: ....?\nAI:....']
```
It should also process the chat history correctly.
But I always get this answer to my prompt:
```
Since the previous chat history is not available, I can't tell what you said last. Please give me more information or ask your question again so I can help you further.
```
|
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| 1,903,035,815 |
PR_kwDOIPDwls5ark79
| 10,782 |
Python: Add iterator support to chat history base class
|
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[] | null | 3 | 2023-09-19T13:47:28 | 2023-09-20T01:57:24 | 2023-09-20T01:57:23 |
CONTRIBUTOR
| null |
- **Description:** Add Python iterator support for chat messages in `BaseChatMessageHistory`. This makes any chat history class iterable on its message list. Here is my use-case that triggered this change.
I have a `ConversationBufferMemory` that is backed by a `FileChatMessageHistory`:
```python
vectorstore = get_vectorstore()
chat_history = FileChatMessageHistory("chat_memory_path/session.json")
memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=chat_history, return_messages=True)
qa = ConversationalRetrievalChain.from_llm(
get_chat_model(),
vectorstore.as_retriever(),
return_source_documents=True,
memory=memory,
)
```
where `get_chat_model()` returns a LLM and `vectorstore` is your favorite Vector store.
A problem occurs when `BaseConversationalRetrievalChain._get_chat_history()` is called. In my setup, `chat_history` is an instance of `FileChatMessageHistory` and `_get_chat_history()` tries to create an iterator from it (see this [line](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/conversational_retrieval/base.py#L37)). I could supply my own callable version when building the chain, but it kind of made sense to me that a chat history would be iterable.
I'm looking for input on how to make this pass in `mypy` since it complains with the following:
```
langchain/memory/chat_message_histories/in_memory.py:10: error: Definition of "__iter__" in base class "BaseChatMessageHistory" is incompatible with definition in base class "BaseModel" [misc]
```
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I_kwDOIPDwls5xbPGZ
| 10,781 |
Vector store-backed retriever -> allow using search_kwargs in get_relevant_documents function (PGVector)
|
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[] | null | 2 | 2023-09-19T13:19:02 | 2023-12-26T16:05:07 | 2023-12-26T16:05:06 |
NONE
| null |
### Feature request
This is the current implementation. You get your Vectorstore and turn it into a retriever. You pass search kwargs into the as_retriever method.
```
retriever = db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .5})
docs = retriever.get_relevant_documents("what did he say about ketanji brown jackson")
```
Is this as intended? I think it´s not the best way. We use PGVector and use it in a API where we pass metadata to do filtered search in the vectorstore.
How I think it should look like:
```
docs = retriever.get_relevant_documents("what did he say about ketanji brown jackson", search_kwargs={"score_threshold": .5})
```
So instead of using it in the constructor, I would rather prefer it in `get_relevant_documents` method.
### Motivation
I don´t want to create a new retriever instance every request and rather have `get_relevant_documents` more dynamic.
### Your contribution
As far as I can see this should be doable by passing some kwargs around. I could probably implement this if I am allowed to.
|
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PR_kwDOIPDwls5arS4M
| 10,780 |
Add option to use SharePoint folder ID instead of folder path
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[] | null | 1 | 2023-09-19T13:13:22 | 2023-09-23T18:07:51 | null |
NONE
| null |
* Description: Provides user with the option to use folder ID to get the SharePoint folder. Updated notebook to show use of this optional feature. Also fixed a typo in the url to get Subsite ID in the notebook (url was missing 'sites/' before ),
* Issue: GraphAPI will often return "No Resource Found" error when using the folder path. This is as far as I can tell a bug on the GraphAPI and not user related. This can be circumvented by using the folder ID and get_item() method instead of the folder path and get_item_by_path() method,
* Dependencies: None,
* Tag maintainer: [@baskaryan](https://github.com/baskaryan),
* Twitter handle: @WRhetoric
|
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I_kwDOIPDwls5xbDBg
| 10,779 |
Make tools async by default unless user overrides `_arun`
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[] | null | 2 | 2023-09-19T12:52:13 | 2023-09-19T13:04:35 | 2023-09-19T13:04:03 |
CONTRIBUTOR
| null |
### Feature request
Most of the tools are currently sync tools, so if an async agent executor calls them tools raise `NotImplementedError`
My suggestion is to change `_arun` method of the `langchain.tools.base.BaseTool` class with a bolierplate code asyncer. An [example is given in the gh issues](https://github.com/langchain-ai/langchain/issues/5011#issuecomment-1725356727).
Asyncer is a small lib developped by @tiangolo (FastApi creator) that can convert a sync code to async (runs in worker threads)
This way by default we would have an async version of any tool. Tool implementors can always override `_arun` method in their implementation providing more native async support. But otherwise we would have a default async implementation.
### Motivation
In production systems we need to scale the requests to our langchain app, thus this means servin cconcurrently to many users. In python for IO Bound operations this is best done with async libs and frameworks. However all parts of the code has to be async, including LLMs + tools ..etc
Since many tools lack support for `_arun` method, this enhancement will provide default async support for all tools.
### Your contribution
Yes, I'll create a PR for this.
|
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I_kwDOIPDwls5xa2Mr
| 10,778 |
Using StructuredTool with agent
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[] | null | 18 | 2023-09-19T12:21:53 | 2023-12-28T16:05:47 | 2023-12-28T16:05:47 |
NONE
| null |
### System Info
Using this Code I am getting this error:
TypeError: Chain.__call__() got an unexpected keyword argument 'question'
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from pydantic import BaseModel, Field
from langchain.chat_models import ChatOpenAI
from langchain.agents import Tool
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.tools import StructuredTool
from dotenv import load_dotenv
class DocumentInput(BaseModel):
question: str = Field()
load_dotenv()
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
tools = []
files = [
# https://abc.xyz/investor/static/pdf/2023Q1_alphabet_earnings_release.pdf
{
"name": "alphabet-earnings",
"path": "/Users/harrisonchase/Downloads/2023Q1_alphabet_earnings_release.pdf",
},
# https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update
{
"name": "tesla-earnings",
"path": "/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf",
},
]
for file in files:
loader = PyPDFLoader(file["path"])
pages = loader.load_and_split()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(pages)
embeddings = OpenAIEmbeddings()
retriever = FAISS.from_documents(docs, embeddings).as_retriever()
# Wrap retrievers in a Tool
tools.append(
StructuredTool.from_function(
args_schema=DocumentInput,
name=file["name"],
description=f"useful when you want to answer questions about {file['name']}",
func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),
)
)
llm = ChatOpenAI(
temperature=0,
model="gpt-3.5-turbo-0613",
)
agent = initialize_agent(
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
tools=tools,
llm=llm,
verbose=True,
)
agent({"input": "did alphabet or tesla have more revenue?"})
### Expected behavior
Compare the two documents!
|
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PR_kwDOIPDwls5aq7Wa
| 10,777 |
Provide option to use folder_id instead of folder_path
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[] | null | 1 | 2023-09-19T12:11:20 | 2023-09-19T12:56:29 | 2023-09-19T12:56:29 |
NONE
| null |
- **Description:** Provides user with the option to use folder ID to get the SharePoint folder. Updated notebook to show use of this optional feature. Also fixed a typo in the url to get Subsite ID in the notebook (url was missing 'sites/' before <site-name>),
- **Issue:** GraphAPI will often return "No Resource Found" error when using the folder path. This is as far as I can tell a bug on the GraphAPI and not user related. This can be circumvented by using the folder ID and get_item() method instead of the folder path and get_item_by_path() method,
- **Dependencies:** None,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:** @WRhetoric
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Add Minimax chat model
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[] | null | 3 | 2023-09-19T12:00:46 | 2023-09-20T03:56:26 | 2023-09-20T03:43:49 |
CONTRIBUTOR
| null |
resolve the merging issues for https://github.com/langchain-ai/langchain/pull/6757
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| 1,902,794,499 |
PR_kwDOIPDwls5aqwVd
| 10,775 |
chore: allow using dataclasses_json dependency v0.6.0
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[] | null | 1 | 2023-09-19T11:46:46 | 2023-09-25T16:19:44 | 2023-09-19T23:22:36 |
CONTRIBUTOR
| null |
**Description:** upgrade the `dataclasses_json` dependency to its latest version ([no real breaking change](https://github.com/lidatong/dataclasses-json/releases/tag/v0.6.0) if used correctly), while allowing previous version to not break other users' setup
**Issue:** I need to use the latest version of that dependency in my project, but `langchain` prevents it.
Note: it looks like running `poetry lock --no-update` did some changes to the lockfiles as it was the first time it was with the `macosx_11_0_arm64` architecture 🤷
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| 1,902,771,820 |
PR_kwDOIPDwls5aqryX
| 10,774 |
make auto-setting the encodings optional, alow explicitly setting it
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[] | null | 3 | 2023-09-19T11:32:44 | 2023-09-20T06:22:43 | 2023-09-19T19:59:53 |
CONTRIBUTOR
| null |
I was trying to use web loaders on some spanish documentation (e.g. [this site](https://www.fromdoppler.com/es/mailing-tendencias/), but the auto-encoding introduced in https://github.com/langchain-ai/langchain/pull/3602 was detected as "MacRoman" instead of the (correct) "UTF-8".
To address this, I've added the ability to disable the auto-encoding, as well as the ability to explicitly tell the loader what encoding to use.
- **Description:** Makes auto-setting the encoding optional in `WebBaseLoader`, and introduces an `encoding` option to explicitly set it.
- **Dependencies:** N/A
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @czue
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| 1,902,666,640 |
I_kwDOIPDwls5xaGOQ
| 10,773 |
In xcode, use swift to call Python code and introduce Ollama error
|
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[] | null | 5 | 2023-09-19T10:22:23 | 2023-12-27T16:05:03 | 2023-12-27T16:05:03 |
NONE
| null |
### System Info
I am developing a macOS application that uses Swift to call Python. To achieve this, I have integrated PythonKit, which also works properly. When I followed the integration of Ollama guided by Langchain, I encountered a problem and reported an error in 'from langchain.llms import Ollama'. The subsequent code cannot execute properly, but I ran it on the terminal without any issues. By setting environment variables, I managed to run both the Python environment and the terminal as 3.11.5, And I can successfully find olama.py under langchain/llms under site packages. I have tried installing TextLoader, which is directly installed under site packages and can import TextLoader normally. I guess it may not be possible to find deeper level packages. Can anyone encounter similar problems and help me solve them or provide some ideas? Thank you
### 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 langchain.llms import Ollama
print('hello')
### Expected behavior
print('hello')
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| 1,902,640,258 |
PR_kwDOIPDwls5aqPE1
| 10,771 |
add clustered vearch in langchain
|
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[] | null | 2 | 2023-09-19T10:05:36 | 2023-09-20T04:22:23 | 2023-09-20T04:22:23 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,902,432,993 |
I_kwDOIPDwls5xZNLh
| 10,770 |
lib/python3.10/site-packages/langchain/agents/conversational/output_parser.py", line 26, in parse raise OutputParserException(f"Could not parse LLM output: `{text}`") langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `Do I need to use a tool? No
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[] | null | 3 | 2023-09-19T08:05:47 | 2023-12-26T16:05:21 | 2023-12-26T16:05:20 |
NONE
| null |
### System Info
langchain==0.0.249
Python 3.10.9
### 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
import re
import json
from langchain.document_loaders import CSVLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import SpacyTextSplitter
from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import TextLoader
from langchain.agents import initialize_agent, tool
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
# VectorDBQA 更新为 RetrievalQA
from langchain.chains import RetrievalQA
llm = OpenAI(temperature=0)
loader = TextLoader('./data/faq/ecommerce_faq.txt')
documents = loader.load()
text_splitter = SpacyTextSplitter(chunk_size=256, pipeline="zh_core_web_sm")
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = FAISS.from_documents(texts, embeddings)
faq_chain = VectorDBQA.from_chain_type(
llm=llm, vectorstore=docsearch, verbose=True)
product_loader = CSVLoader('./data/faq/ecommerce_products.csv')
product_documents = product_loader.load()
product_text_splitter = CharacterTextSplitter(chunk_size=1024, separator="\n")
product_texts = product_text_splitter.split_documents(product_documents)
product_search = FAISS.from_documents(product_texts, OpenAIEmbeddings())
product_chain = VectorDBQA.from_chain_type(
llm=llm, vectorstore=product_search, verbose=True)
ORDER_1 = "20230101ABC"
ORDER_2 = "20230101EFG"
ORDER_1_DETAIL = {
"order_number": ORDER_1,
"status": "已发货",
"shipping_date": "2023-01-03",
"estimated_delivered_date": "2023-01-05",
}
ORDER_2_DETAIL = {
"order_number": ORDER_2,
"status": "未发货",
"shipping_date": None,
"estimated_delivered_date": None,
}
answer_order_info = PromptTemplate(
template="请把下面的订单信息回复给用户： \n\n {order}?", input_variables=["order"]
)
answer_order_llm = LLMChain(llm=ChatOpenAI(
temperature=0), prompt=answer_order_info)
@tool("Search Order", return_direct=True)
def search_order(input: str) -> str:
"""useful for when you need to answer questions about customers orders"""
pattern = r"\d+[A-Z]+"
match = re.search(pattern, input)
order_number = input
if match:
order_number = match.group(0)
else:
return "请问您的订单号是多少？"
if order_number == ORDER_1:
return answer_order_llm.run(json.dumps(ORDER_1_DETAIL))
elif order_number == ORDER_2:
return answer_order_llm.run(json.dumps(ORDER_2_DETAIL))
else:
return f"对不起，根据{input}没有找到您的订单"
@tool("FAQ")
def faq(intput: str) -> str:
""""useful for when you need to answer questions about shopping policies, like return policy, shipping policy, etc."""
return faq_chain.run(intput)
@tool("Recommend Product")
def recommend_product(input: str) -> str:
""""useful for when you need to search and recommend products and recommend it to the user"""
return product_chain.run(input)
tools = [
search_order,
recommend_product,
faq
]
chatllm = ChatOpenAI(temperature=0)
memory = ConversationBufferMemory(
memory_key="chat_history", return_messages=True)
conversation_agent = initialize_agent(
tools,
chatllm,
agent="conversational-react-description",
memory=memory,
verbose=True
)
question = "请问你们的货，能送到三亚吗？大概需要几天？"
result = conversation_agent.run(question)
print(result)
### Expected behavior
Hope no error is reported
|
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| 1,902,355,503 |
PR_kwDOIPDwls5apSgH
| 10,769 |
Add Vald as VectorStore
|
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[] | null | 1 | 2023-09-19T07:14:39 | 2023-09-19T23:32:05 | 2023-09-19T23:32:05 |
CONTRIBUTOR
| null |
**Description:**
Support of Vald as vector store.
Integration tests and example notebook are included.
- Vald: https://github.com/vdaas/vald
|
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PR_kwDOIPDwls5apAhH
| 10,767 |
Remembrall Integration
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[] | null | 2 | 2023-09-19T06:15:08 | 2023-09-19T15:36:33 | 2023-09-19T15:36:32 |
CONTRIBUTOR
| null |
- **Description:** Added integration instructions for Remembrall.
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @raunakdoesdev
Fun fact, this project originated at the Modal Hackathon in NYC where it won the Best LLM App prize sponsored by Langchain. Thanks for your support 🦜
|
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| 1,902,074,094 |
PR_kwDOIPDwls5aoWB0
| 10,765 |
Allow extra variables when invoking prompt templates
|
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[] | null | 1 | 2023-09-19T02:04:41 | 2023-09-19T03:08:55 | 2023-09-19T03:08:54 |
CONTRIBUTOR
| null |
Makes chaining easier as many maps have extra properties.
@baskaryan @hwchase17
|
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I_kwDOIPDwls5xX1HN
| 10,764 |
Issue with BaseChatMessageHistory Attribute Scope
|
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[] | null | 2 | 2023-09-19T02:02:29 | 2023-12-28T16:05:52 | 2023-12-28T16:05:51 |
CONTRIBUTOR
| null |
### System Info
LangChain v0.0.294
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I've recently encountered an issue that took a bit of time to isolate and identify. I have a service that utilizes a chain for conversations, where multiple instances are created of the same chain to manage responses to all users. However, I observed an issue where messages started to get mixed up.
Initially, I suspected the issue might be related to the websocket, but upon further investigation, I realized that the root cause seems to be the way `BaseChatMessageHistory` is declared. It appears that the messages list is implemented as a class attribute rather than an instance attribute. This seems to be causing memory to be shared across all instances, which doesn't align with the expected behavior and is causing the message mix-up that I observed.
I wanted to reach out to understand if there is a specific reason for implementing the messages list as a class attribute? In my perspective, modifying it to be an instance attribute might prevent such issues from occurring, and I believe it could potentially be considered a bug in its current state. I'm open to collaborating on a fix and would be happy to submit a PR to address this.
### Expected behavior
Just make it an instance attribute.
|
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PR_kwDOIPDwls5aoS0Z
| 10,763 |
Add support for HTTP PUT in the open api agent prompt
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[] | null | 1 | 2023-09-19T01:48:45 | 2023-09-19T23:37:21 | 2023-09-19T23:37:21 |
CONTRIBUTOR
| null |
**Description:** This PR adds HTTP PUT support for the langchain openapi agent toolkit by leveraging existing structure and HTTP put request wrapper. The PUT method is almost identical to HTTP POST but should be idempotent and therefore tighter than POST which is not idempotent. Some APIs may consider to use PUT instead of POST which is unfortunately not supported with the current toolkit yet.
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PR_kwDOIPDwls5aoSAt
| 10,762 |
Merge metadata + tags in config
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[] | null | 1 | 2023-09-19T01:43:38 | 2023-09-19T15:00:31 | 2023-09-19T15:00:30 |
COLLABORATOR
| null |
Think these should be a merge/update rather than overwrite
|
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PR_kwDOIPDwls5aoON2
| 10,761 |
add project section to pyproject.toml, closes #10760
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[] | null | 2 | 2023-09-19T01:20:05 | 2023-09-19T04:59:32 | 2023-09-19T04:59:32 |
NONE
| null |
- **Description:** add project section to `pyproject.toml`
- **Issue:** closes #10760
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
- **Twitter handle:** None
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I_kwDOIPDwls5xXr0l
| 10,760 |
pip install from source creates UNKNOWN package
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[] | null | 2 | 2023-09-19T01:16:26 | 2023-09-19T04:59:02 | 2023-09-19T04:59:02 |
NONE
| null |
### System Info
Python Version: `Python 3.10.12`
LangChain Version: `master`
### Who can help?
@hwchase17
### 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
Following [Getting Started - Installing - From Source](https://python.langchain.com/docs/get_started/installation#from-source)
```
python3 -m pip install -e .
```
Yields an install of `UNKNOWN` package due to missing `[project]` section in the `pyproject.toml`
```
Successfully built UNKNOWN
Installing collected packages: UNKNOWN
Attempting uninstall: UNKNOWN
Found existing installation: UNKNOWN 0.0.0
Uninstalling UNKNOWN-0.0.0:
Successfully uninstalled UNKNOWN-0.0.0
Successfully installed UNKNOWN-0.0.0
```
### Expected behavior
```
Successfully built langchain
Installing collected packages: langchain
Successfully installed langchain-0.0.294
```
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PR_kwDOIPDwls5aoMH3
| 10,759 |
keep reference to old embeddings base
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PR_kwDOIPDwls5aoICG
| 10,757 |
add agent components v2
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PR_kwDOIPDwls5aoALZ
| 10,755 |
Start RunnableBranch callback tags with 1 instead of 0
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CONTRIBUTOR
| null |
Changes to match `RunnableSequences`
@eyurtsev
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Update routing cookbook to include a RunnableBranch example
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| false | null |
[] | null | 2 | 2023-09-19T00:13:02 | 2023-09-19T14:59:56 | 2023-09-19T14:59:55 |
CONTRIBUTOR
| null |
~~Because we can't pass extra parameters into a prompt, we have to prepend a function before the runnable calls in the branch and it's a bit less elegant than I'd like.~~
All good now that #10765 has landed!
@eyurtsev @hwchase17
|
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| 1,901,952,494 |
PR_kwDOIPDwls5an7GX
| 10,753 |
fix broken link in docugami loader docs
|
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[] | null | 1 | 2023-09-19T00:08:49 | 2023-09-19T04:56:33 | 2023-09-19T04:56:33 |
CONTRIBUTOR
| null |
Just fixing the link to the self query retriever in docugami loader docs
|
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PR_kwDOIPDwls5anvWP
| 10,752 |
fix recursive loader
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[] | null | 1 | 2023-09-18T23:26:06 | 2023-09-20T15:16:55 | 2023-09-20T15:16:54 |
COLLABORATOR
| null |
maintain same base url throughout recursion, yield initial page, fixing recursion depth tracking
|
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PR_kwDOIPDwls5anoSD
| 10,751 |
bump 294
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[] | null | 1 | 2023-09-18T22:57:07 | 2023-09-18T23:04:04 | 2023-09-18T23:04:03 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5annOX
| 10,750 |
Allow 3.5-turbo instruct models in the OpenAI LLM class
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[] | null | 1 | 2023-09-18T22:51:33 | 2023-09-18T22:55:14 | 2023-09-18T22:55:13 |
CONTRIBUTOR
| null |
@baskaryan @hwchase17
|
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| 1,901,803,694 |
I_kwDOIPDwls5xWziu
| 10,749 |
LLMSingleActionAgent Doesnot provide Expected response
|
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[] | null | 2 | 2023-09-18T21:51:30 | 2023-12-25T16:05:44 | 2023-12-25T16:05:44 |
NONE
| null |
### Issue you'd like to raise.
I am using LLMSingleActionAgent with ReAct Prompt. Most of the times it fails to give the proper response to the input. For example, when I greet the agent, it is not greeting back most of the times. Below is the response snippet
**Below is my Agent code:**
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
**Part of Prompt:**
You have access to the following tools. you can use tools, your Previous conversation history to answer the questions
{tools}
Previous Conversation history:
{history}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, can be one of [{tool_names}] or {history}
Action Input: the input to the action
Observation: the result of the action
Thought: I now know the final answer
Final Answer: the final answer to the original input question
**Output Parser:**
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
### Suggestion:
_No response_
|
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| 1,901,747,854 |
PR_kwDOIPDwls5anNyN
| 10,748 |
mv data bricks sql page
|
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[] | null | 1 | 2023-09-18T21:10:10 | 2023-09-18T21:54:43 | 2023-09-18T21:54:42 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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| 1,901,665,767 |
I_kwDOIPDwls5xWR3n
| 10,747 |
LLaMa-2-13B-chat deployed on azureml studio giving out of context response
|
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[] | null | 3 | 2023-09-18T20:12:19 | 2023-12-26T16:05:31 | 2023-12-26T16:05:30 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Recently, we re-deployed the llam2-13-B chat model on azure ML Studio, whenever we are trying to do in-contex learning (RAG QA), always replied back with out of context response.
Currently we are passing request payload as using langchain:
System prompt: sys msg + context
Human prompt: question
Response: out of context response
2nd variation:
Human prompt: context + question
Response: junk answers like \n
Any suggestions who have experienced?
And, we are also followed the required input format.
### Suggestion:
_No response_
|
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| 1,901,643,044 |
PR_kwDOIPDwls5am22_
| 10,746 |
Substring support for similarity_search_with_score
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[] | null | 1 | 2023-09-18T19:55:19 | 2023-09-20T03:32:44 | 2023-09-20T03:32:44 |
CONTRIBUTOR
| null |
**Description:** Possible to filter with substrings in similarity_search_with_score, for example: filter={'user_id': {'substring': 'user'}}
|
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| 1,901,534,677 |
I_kwDOIPDwls5xVx3V
| 10,745 |
Support of Gradient.ai LLM
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[] | null | 1 | 2023-09-18T18:42:19 | 2023-09-22T09:33:42 | 2023-09-22T09:33:42 |
CONTRIBUTOR
| null |
### Feature request
I propose to add the Python client of gradient.ai as a `LLM`.
`gradient_ai.py` under https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/llms
```python
class GradientAI(LLM):
# client for Large Language Models on gradient.ai
#
```
See: API docs
https://docs.gradient.ai/reference/listmodels
### Motivation
Gradient.ai offers fine-tuning and inference of models without paying for the server costs.
I fine-tuned a model with a specific RAG template based on llama2 on gradient.ai, now i want to use it from Langchain.
### Your contribution
I would submit a PR.
|
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| 10,744 |
Cannot use LangChain Tools with GPT4ALL and LLaMA model
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[] | null | 2 | 2023-09-18T18:28:42 | 2023-10-14T15:55:25 | null |
NONE
| null |
### System Info
LangChain 0.0.292
Python 3.11.5
GPT4ALL with LLAMA q4_0 3b model running on CPU
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
My code is:
```from langchain.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.agents import AgentType, initialize_agent
from langchain.tools import Tool
from pydantic import BaseModel, Field
class JokeInput(BaseModel):
confidence: float = Field(default=0.0)
import pyjokes
import langchain
langchain.debug = True
import os
os.environ["LANGCHAIN_TRACING"] = "true" # If you want to trace the execution of the program, set to "true"
# There are many CallbackHandlers supported, such as
# from langchain.callbacks.streamlit import StreamlitCallbackHandler
callbacks = [StreamingStdOutCallbackHandler()]
model = GPT4All(model=".cache/llama-2-7b-chat.ggmlv3.q4_0.bin", n_threads=6)
# Generate text. Tokens are streamed through the callback manager.
tools = []
tools.append(
Tool.from_function(
func=pyjokes.get_joke,
name="Joke",
description="useful for when you need to tell the user a joke",
args_schema=JokeInput
)
)
# Init the agent
agent = initialize_agent(
tools, model, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
# Start the agent
while True:
user_prompt = input("You: ")
agent(user_prompt, callbacks=callbacks)```
```
### Expected behavior
It should use the Joke tool and return a joke, but instead it gives me this error:
```
Found model file at .cache/llama-2-7b-chat.ggmlv3.q4_0.bin
llama.cpp: loading model from .cache/llama-2-7b-chat.ggmlv3.q4_0.bin
llama_model_load_internal: format = ggjt v3 (latest)
llama_model_load_internal: n_vocab = 32000
llama_model_load_internal: n_ctx = 2048
llama_model_load_internal: n_embd = 4096
llama_model_load_internal: n_mult = 256
llama_model_load_internal: n_head = 32
llama_model_load_internal: n_head_kv = 32
llama_model_load_internal: n_layer = 32
llama_model_load_internal: n_rot = 128
llama_model_load_internal: n_gqa = 1
llama_model_load_internal: rnorm_eps = 5.0e-06
llama_model_load_internal: n_ff = 11008
llama_model_load_internal: freq_base = 10000.0
llama_model_load_internal: freq_scale = 1
llama_model_load_internal: ftype = 2 (mostly Q4_0)
llama_model_load_internal: model size = 7B
llama_model_load_internal: ggml ctx size = 3615.73 MB
llama_model_load_internal: mem required = 4013.73 MB (+ 1024.00 MB per state)
llama_new_context_with_model: kv self size = 1024.00 MB
You: Tell me a joke
Failed to load default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions?name=default (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7ff8fc87d290>: Failed to establish a new connection: [Errno 111] Connection refused'))
[chain/start] [1:chain:AgentExecutor] Entering Chain run with input:
{
"input": "Tell me a joke"
}
[chain/start] [1:chain:AgentExecutor > 2:chain:LLMChain] Entering Chain run with input:
{
"input": "Tell me a joke",
"agent_scratchpad": "",
"stop": [
"Observation:"
]
}
[llm/start] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:GPT4All] Entering LLM run with input:
{
"prompts": [
"System: Answer the following questions as best you can. You have access to the following tools:\n\nJoke: useful for when you need to tell the user a joke\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the \"action\" field are: Joke\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{\n \"action\": $TOOL_NAME,\n \"action_input\": $INPUT\n}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin! Reminder to always use the exact characters `Final Answer` when responding.\nHuman: Tell me a joke"
]
}
$JSON_BLOB[llm/end] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:GPT4All] [18.05s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "$JSON_BLOB",
"generation_info": null
}
]
],
"llm_output": null,
"run": null
}
[chain/end] [1:chain:AgentExecutor > 2:chain:LLMChain] [18.05s] Exiting Chain run with output:
{
"text": "$JSON_BLOB"
}
[chain/error] [1:chain:AgentExecutor] [18.05s] Chain run errored with error:
"OutputParserException('Could not parse LLM output: $JSON_BLOB')"
Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7ff8fc24f9d0>: Failed to establish a new connection: [Errno 111] Connection refused'))
Traceback (most recent call last):
File "/mnt/0A956B927E2FFFE8/Code/jarvis/.venv/lib/python3.11/site-packages/langchain/agents/chat/output_parser.py", line 27, in parse
raise ValueError("action not found")
ValueError: action not found
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/mnt/0A956B927E2FFFE8/Code/jarvis/langchain_try.py", line 41, in <module>
agent(user_prompt, callbacks=callbacks)
File "/mnt/0A956B927E2FFFE8/Code/jarvis/.venv/lib/python3.11/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/mnt/0A956B927E2FFFE8/Code/jarvis/.venv/lib/python3.11/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/mnt/0A956B927E2FFFE8/Code/jarvis/.venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 1122, in _call
next_step_output = self._take_next_step(
^^^^^^^^^^^^^^^^^^^^^
File "/mnt/0A956B927E2FFFE8/Code/jarvis/.venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 930, in _take_next_step
raise e
File "/mnt/0A956B927E2FFFE8/Code/jarvis/.venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 919, in _take_next_step
output = self.agent.plan(
^^^^^^^^^^^^^^^^
File "/mnt/0A956B927E2FFFE8/Code/jarvis/.venv/lib/python3.11/site-packages/langchain/agents/agent.py", line 532, in plan
return self.output_parser.parse(full_output)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/0A956B927E2FFFE8/Code/jarvis/.venv/lib/python3.11/site-packages/langchain/agents/chat/output_parser.py", line 42, in parse
raise OutputParserException(f"Could not parse LLM output: {text}")
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: $JSON_BLOB
```
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PR_kwDOIPDwls5amO9F
| 10,743 |
Updated return parameter of YouTubeSearchTool
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[] | null | 1 | 2023-09-18T17:52:24 | 2023-09-20T04:01:03 | 2023-09-20T00:04:07 |
CONTRIBUTOR
| null |
**Description:**
changed return parameter of YouTubeSearchTool
1. changed the returning links of youtube videos by adding prefix "https://www.youtube.com", now this will return the exact links to the videos
2. updated the returning type from 'string' to 'list', which will be more suited for further processings
**Issue:**
Fixes #10742
**Dependencies:**
None
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** changed return parameter of YouTubeSearchTool
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** None
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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I_kwDOIPDwls5xVd4E
| 10,742 |
Update return parameter of YouTubeSearchTool
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[] | null | 1 | 2023-09-18T17:47:53 | 2023-09-20T00:04:08 | 2023-09-20T00:04:08 |
CONTRIBUTOR
| null |
### Feature request
Return the Youtube video links in full format like `https://www.youtube.com/watch?v=VIDEO_ID`
Currently the links are like `/watch?v=VIDEO_ID`
Return the links as List like `['link1, 'link2']`
Currently it is returning the whole list as string ` "['link1, 'link2']" `
### Motivation
If the links returned are exact same as **direct links to youtube in a list** rather than a string, i can avoid the hustle and bustle of processing it agian to convert to the required format
### Your contribution
I will change the code a bit and pull it.
|
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PR_kwDOIPDwls5alTiZ
| 10,740 |
bump 293
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COLLABORATOR
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I_kwDOIPDwls5xTji5
| 10,739 |
Issue: How to update Pinecone vectors data using langchain
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[] | null | 4 | 2023-09-18T13:31:20 | 2023-10-10T09:22:54 | null |
NONE
| null |
### Issue you'd like to raise.
I am using langchain with Pinecone to push the embedding into the Pincone vector db. using below code. But I see that there is too many IDs created for the single PDF file in Pincone Browswer(My PDF file more than 250 pages). I want to update my PDF for the same Index. How can I do ?
`from langchain.document_loaders import UnstructuredPDFLoader, OnlinePDFLoader, PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma, Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import pinecone
import os
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
loader = UnstructuredPDFLoader("example.pdf")
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
texts = text_splitter.split_documents(data)
type(texts)
OPENAI_API_KEY = "sk-xxxxx"
PINECONE_API_KEY = "xxxx"
PINECONE_API_ENV = "us-west-gcp-free"
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
pinecone.init(
api_key=PINECONE_API_KEY,
environment=PINECONE_API_ENV
)
index_name = "langchaintest"
docsearch = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name)`
I want to update my vectors Due to multiple IDs I am not able to get the ID to update the New one PDF file
### Suggestion:
ID should be one for the single PDF file.
|
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PR_kwDOIPDwls5akcl4
| 10,738 |
Bug fixes for runnables
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COLLABORATOR
| null |
- tools invoked in async methods would not work due to missing await
- RunnableSequence.stream() was creating an extra root run by mistake, and it can simplified due to existence of default implementation for .transform()
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
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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.
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I_kwDOIPDwls5xTc14
| 10,737 |
Issue: <Langchain Retriever Agent Function Not Passing “ChatMessageHistory” When Called in Console - Python>
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[] | null | 2 | 2023-09-18T13:17:46 | 2023-09-21T10:06:11 | 2023-09-21T10:06:11 |
NONE
| null |
### Issue you'd like to raise.
Hello everyone,
I am facing an issue with a Python function that I have created to handle some chat functionality with the ChatGPT model. The function is part of a bigger project that aims to use Pinecone to search and retrieve similar documents.
Here is a simplified version of my code:
> def pineconeGPT(system, user, questions, answers, instruction, metadata={}):
llm = ChatOpenAI(model='gpt-3.5-turbo-16k', temperature=0, openai_api_key="key")
embeddings = OpenAIEmbeddings(
openai_api_key="key", model="text-embedding-ada-002")
pinecone.init(api_key='key', environment='region')
index = Pinecone.from_existing_index('index_name', embeddings)
history = ChatMessageHistory()
if len(questions) > 0:
for i in range(len(questions)):
history.add_ai_message(questions[i])
history.add_user_message(answers[i])
retriever = index.as_retriever(
search_type="similarity",
search_kwargs={'filter': metadata}
)
pineconeIndex = create_retriever_tool(
retriever,
"search_pinecone",
"Searches and returns documents regarding the similarity."
)
tools = [pineconeIndex]
system_message = SystemMessage(
content=(system)
prompt = OpenAIFunctionsAgent.create_prompt(
system_message=system_message
)
agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
memory=ConversationBufferMemory(chat_memory=history),
verbose=True,
return_intermediate_steps=False,
return_only_outputs=False)
result = agent_executor({"input" : instruction})
The history is included in the result but for example:
If the question is: What is your name?
Answer: My name is Bob
And the instruction says "what is my name"
ChatGPT response is saying I have not provided name.
This suggest that ChatGPT has not being passed the history by the agent.
I would appreciate any insights on what might be causing this issue and how to resolve it. Thank you in advance!
### Suggestion:
_No response_
|
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| 1,900,884,697 |
I_kwDOIPDwls5xTTLZ
| 10,736 |
Typing error in WebResearchRetriever
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[] | null | 2 | 2023-09-18T12:58:58 | 2023-09-19T07:29:37 | 2023-09-19T07:29:37 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.292
The type of text_splitter must be TextSplitter and not RecursiveCharacterTextSplitter
```
class WebResearchRetriever(BaseRetriever):
...
text_splitter: **TextSplitter** = Field(
RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50),
description="Text splitter for splitting web pages into chunks",
)
```
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use WebResearchRetriever with a simple TextRetriver. It's run.
Now `make lint` => Error
### Expected behavior
No error with `make lint`
Pull request [here](https://github.com/langchain-ai/langchain/pull/10734)
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I_kwDOIPDwls5xTKE5
| 10,735 |
bug: map_reduce (RetrievalQAWithSourcesChain.from_chain_type) with embeddings - reproducible
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[] | null | 6 | 2023-09-18T12:38:22 | 2023-12-18T23:56:32 | null |
NONE
| null |
### System Info
```
langchain==0.0.291
chromadb==0.4.10
Python 3.10.0
22.6.0 Darwin Kernel
```
### Who can help?
@agola11 @hwchase17 bug map_reduce - reproducible
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
## Description
The bug arrises when using `map_reduce` with `RetrievalQAWithSourcesChain.from_chain_type` and `Chroma,` and adding more than 1 document.
Specifically, we have `paper.pdf` and `paper2.pdf`.
`paper.pdf` talks about `'x=3'`
`paper2.pdf` talks about `'y=5'`
*This is key*: Embedding and searching for each one individually with just *one* works perfectly.
If you embed and add the two (or anything more than 1) back to back, and you search with `map_reduce` get this error:
(NOTE: *this is also key*, for the same embedding, if you switch to using `RetrievalQAWithSourcesChain.from_chain_type` with `stuff`, it also *works perfectly*)
## Example of Error:
```
--------------> PDF1
[Document(page_content='This is a sample paper!This paper talks about x.In this case, we are going to say that "x = 3"', metadata={'source': '/Users/user/Desktop/paper.pdf', 'page': 0, 'start_index': 0})]
Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1
========================
{'question': 'what is x?', 'answer': 'x = 3.\n', 'sources': '/Users/user/Desktop/paper.pdf', 'source_documents': [Document(page_content='This is a sample paper!This paper talks about x.In this case, we are going to say that "x = 3"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper.pdf', 'start_index': 0})]}
========================
--------------> PDF2
[Document(page_content='This is another sample paper! This paper talks about y. In this case, we are going to say that "y = 5"', metadata={'source': '/Users/user/Desktop/paper2.pdf', 'page': 0, 'start_index': 0})]
Number of requested results 4 is greater than number of elements in index 2, updating n_results = 2
========================
{'question': 'what is y?', 'answer': 'There is no relevant text provided to answer the question "what is y?"\n', 'sources': '/Users/user/Desktop/paper.pdf', 'source_documents': [Document(page_content='This is another sample paper! This paper talks about y. In this case, we are going to say that "y = 5"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper2.pdf', 'start_index': 0}), Document(page_content='This is a sample paper!This paper talks about x.In this case, we are going to say that "x = 3"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper.pdf', 'start_index': 0})]}
========================
```
Again, -- each one by itself works. (both `paper.pdf` and `paper2.pdf` produces the correct answer individually). Also, moving to the `stuff` chain, produces the correct result for both or more documents.
```
--------------> PDF1
[Document(page_content='This is a sample paper!\r\nThis paper talks about x.\r\nIn this case, we are going to say that "x = 3"', metadata={'source': '/Users/user/Desktop/paper.pdf', 'page': 0, 'start_index': 0})]
Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1
========================
{'question': 'what is x?', 'answer': 'x = 3\n', 'sources': '/Users/user/Desktop/paper.pdf', 'source_documents': [Document(page_content='This is a sample paper!\r\nThis paper talks about x.\r\nIn this case, we are going to say that "x = 3"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper.pdf', 'start_index': 0})]}
========================
--------------> PDF2
[Document(page_content='This is another sample paper!\r\nThis paper talks about y.\r\nIn this case, we are going to say that "y = 5"', metadata={'source': '/Users/user/Desktop/paper2.pdf', 'page': 0, 'start_index': 0})]
Number of requested results 4 is greater than number of elements in index 2, updating n_results = 2
========================
{'question': 'what is y?', 'answer': 'y = 5\n', 'sources': '/Users/user/Desktop/paper2.pdf', 'source_documents': [Document(page_content='This is another sample paper!\r\nThis paper talks about y.\r\nIn this case, we are going to say that "y = 5"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper2.pdf', 'start_index': 0}), Document(page_content='This is a sample paper!\r\nThis paper talks about x.\r\nIn this case, we are going to say that "x = 3"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper.pdf', 'start_index': 0})]}
========================
```
## Reproduce:
Create a llm (OpenAI or AzureChatOpenAI):
1.)
```
llm = AzureChatOpenAI(
temperature=0,
openai_api_base=openai_api_base,
openai_api_version=openai_api_version,
openai_api_key=openai_api_key,
openai_api_type=openai_api_type,
deployment_name=openai_model
)
```
2.) Create embeddings:
```
embeddings = OpenAIEmbeddings(deployment=openai_embedding_model, chunk_size=1)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20, length_function = len, add_start_index = True)
```
3.) Setup Chroma and Retriever
```
db = Chroma(embedding_function=embeddings)
# Again a note: if you replace `map_reduce` with `stuff` -- works perfectly!
qa = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, chain_type="map_reduce", retriever=db.as_retriever(search_kwargs={"k": 4}), return_source_documents=True)
```
4.) Embed the files `db.add_documents()` and search!
```
NOTE: I have tried with multiple PDF loaders and the same problem exists even with plain text -- so this is not the issue
print("--------------> PDF1")
loader = PyPDFium2Loader('/Users/user/Desktop/paper.pdf')
docs = loader.load_and_split(text_splitter)
print(docs)
db.add_documents(docs)
text = 'what is x?'
result = qa({"question": text})
print("========================")
print(result)
print("========================")
print("--------------> PDF2")
loader = PyPDFium2Loader('/Users/user/Desktop/paper2.pdf')
docs = loader.load_and_split(text_splitter)
print(docs)
db.add_documents(docs)
text = 'what is y?'
result = qa({"question": text})
print("========================")
print(result)
print("========================")
```
## Key Summary:
* As you will see, the correct documents are retrieved -- it's just the injections/`map_reduce` that's causing a problem
* With `map_reduce` - if there is a single `Document()` it works fine -- for both `paper.pdf` and `paper2.pdf`
* With `map_reduce`, more than 1 document causes the issue.
* If you use the same embeddings and the same code with `stuff` chain it works perfectly with multiple documents.
## Files Attached for testing:
[paper.pdf](https://github.com/langchain-ai/langchain/files/12647756/paper.pdf)
[paper2.pdf](https://github.com/langchain-ai/langchain/files/12647750/paper2.pdf)
see a self-contained example here:
https://github.com/langchain-ai/langchain/issues/10735#issuecomment-1723595341
### Expected behavior
For `RetrievalQAWithSourcesChain.from_chain_type` to be able to inject the correct documents that it finds with `map_reduce` (similar to `stuff` which works):
```
--------------> PDF1
[Document(page_content='This is a sample paper!\r\nThis paper talks about x.\r\nIn this case, we are going to say that "x = 3"', metadata={'source': '/Users/user/Desktop/paper.pdf', 'page': 0, 'start_index': 0})]
Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1
========================
{'question': 'what is x?', 'answer': 'x = 3\n', 'sources': '/Users/user/Desktop/paper.pdf', 'source_documents': [Document(page_content='This is a sample paper!\r\nThis paper talks about x.\r\nIn this case, we are going to say that "x = 3"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper.pdf', 'start_index': 0})]}
========================
--------------> PDF2
[Document(page_content='This is another sample paper!\r\nThis paper talks about y.\r\nIn this case, we are going to say that "y = 5"', metadata={'source': '/Users/user/Desktop/paper2.pdf', 'page': 0, 'start_index': 0})]
Number of requested results 4 is greater than number of elements in index 2, updating n_results = 2
========================
{'question': 'what is y?', 'answer': 'y = 5\n', 'sources': '/Users/user/Desktop/paper2.pdf', 'source_documents': [Document(page_content='This is another sample paper!\r\nThis paper talks about y.\r\nIn this case, we are going to say that "y = 5"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper2.pdf', 'start_index': 0}), Document(page_content='This is a sample paper!\r\nThis paper talks about x.\r\nIn this case, we are going to say that "x = 3"', metadata={'page': 0, 'source': '/Users/user/Desktop/paper.pdf', 'start_index': 0})]}
========================
```
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PR_kwDOIPDwls5aj2gI
| 10,734 |
Fix typing in WebResearchRetriver
|
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| false | null |
[] | null | 2 | 2023-09-18T11:48:54 | 2023-09-18T15:17:11 | 2023-09-18T15:17:11 |
CONTRIBUTOR
| null |
Hello @hwchase17
**Issue**:
The class WebResearchRetriever accept only RecursiveCharacterTextSplitter, but never uses a specification of this class. I propose to change the type to TextSplitter. Then, the lint can accept all subtypes.
|
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| 1,900,747,180 |
I_kwDOIPDwls5xSxms
| 10,733 |
get_openapi_chain throws error when running example from docs
|
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open
| false | null |
[] | null | 18 | 2023-09-18T11:39:49 | 2023-12-26T04:40:34 | null |
CONTRIBUTOR
| null |
### System Info
pydantic '1.10.0' or '2.3.0'
langchain '0.0.292'
python '3.11.5'
### Who can help?
@hwchase17
@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
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.chains.openai_functions.openapi import get_openapi_chain
chain = get_openapi_chain("https://www.klarna.com/us/shopping/public/openai/v0/api-docs/")
chain("What are some options for a men's large blue button down shirt")
Error message when running with pydantic 1
Unable to parse spec from source https://chat-web3-plugin.alchemy.com/openapi.yaml error
Error message when running with pydantic 2
AttributeError: type object 'OpenAPISpec' has no attribute 'from_url'
### Expected behavior
normal execution without errors
|
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| 1,900,689,023 |
PR_kwDOIPDwls5ajmJL
| 10,732 |
Update ContextCallbackHandler Docstring & metadata key
|
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[] | null | 4 | 2023-09-18T11:02:55 | 2023-09-19T05:04:13 | 2023-09-19T05:04:13 |
CONTRIBUTOR
| null |
- **Description:** Updating URL in Context Callback Docstrings and update metadata key Context CallbackHandler uses to send model names.
- **Issue:** The URL in ContextCallbackHandler is out of date. Model data being sent to Context should be under the "model" key and not "llm_model". This allows Context to do more sophisticated analysis.
- **Dependencies:** None
Tagging @agamble.
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| 1,900,607,849 |
I_kwDOIPDwls5xSPlp
| 10,731 |
data_manager not working with cache init for chatOpenAI
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[] | null | 2 | 2023-09-18T10:15:38 | 2023-12-25T16:05:54 | 2023-12-25T16:05:54 |
NONE
| null |
### System Info
langchain-0.0.292, python-3, vsCode
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.schema import HumanMessage, SystemMessage
from langchain.chat_models import ChatOpenAI
import os
from dotenv import load_dotenv
from gptcache import cache
from gptcache.manager import get_data_manager,manager_factory, CacheBase, VectorBase
from gptcache.similarity_evaluation.distance import SearchDistanceEvaluation
from gptcache.embedding import Onnx
from gptcache.processor.pre import get_messages_last_content
from langchain.chat_models import ChatOpenAI
from gptcache.adapter.langchain_models import LangChainChat
import time
print("Cache loading.....")
onnx = Onnx()
cache_base = CacheBase('mysql', sql_url='your_mysql_url')
vector_base = VectorBase('faiss', dimension=128)
data_manager = get_data_manager(cache_base, vector_base)
cache.init(pre_embedding_func=get_messages_last_content,
data_manager=data_manager,
)
cache.set_openai_key()
load_dotenv()
def generate_res(user_input):
chat = LangChainChat(chat=ChatOpenAI(model_name=os.environ.get('GPT_MODEL'), temperature=0.7, openai_api_key=os.environ.get('OPEN_AI_API_KEY')))
prompt=""
start_time = time.time()
message = [
SystemMessage(content=prompt),
HumanMessage(content=user_input),
]
response = chat(message)
generate_res("hey there!")
print("Time consuming: {:.2f}s".format(time.time() - start_time))
### Expected behavior
I'm trying to store the cache into mysql db using chatopenai and gptcache. but getting this error:
super().__init__(**kwargs)
File "pydantic\main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic\main.py", line 1066, in pydantic.main.validate_model
File "pydantic\fields.py", line 439, in pydantic.fields.ModelField.get_default
File "pydantic\utils.py", line 693, in pydantic.utils.smart_deepcopy
File "C:\Program Files\Python311\Lib\copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 146, in deepcopy
y = copier(x, memo)
^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 146, in deepcopy
y = copier(x, memo)
^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 146, in deepcopy
y = copier(x, memo)
^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 146, in deepcopy
y = copier(x, memo)
^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 146, in deepcopy
y = copier(x, memo)
^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 146, in deepcopy
y = copier(x, memo)
^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\Python311\Lib\copy.py", line 161, in deepcopy
rv = reductor(4)
^^^^^^^^^^^
TypeError: cannot pickle 'module' object
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| 1,900,506,801 |
I_kwDOIPDwls5xR26x
| 10,730 |
Tool request how to know response status code
|
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NONE
| null |
In intermediateSteps, how to get the response status code if I using the tool request_get or post..
|
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| 1,900,370,220 |
I_kwDOIPDwls5xRVks
| 10,729 |
async mongodb chat message history
|
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[] | null | 3 | 2023-09-18T07:53:53 | 2024-01-04T14:01:15 | 2023-12-25T16:06:04 |
NONE
| null |
### Feature request
Switch to motor in the [MongoDBChatMessageHistory](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/memory/chat_message_histories/mongodb.py) class for asyncio support.
### Motivation
There is already a PR #10645 for async Document Loader for MongoDB, this way we can keep consistency in the mongodb clients.
### Your contribution
I already implemented it, if you want I can submit a PR.
|
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PR_kwDOIPDwls5aiURi
| 10,728 |
Sagemaker endpoint capability to inject boto3 client for cross account scenarios
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[] | null | 4 | 2023-09-18T07:15:05 | 2023-10-21T14:56:20 | 2023-09-19T15:06:13 |
CONTRIBUTOR
| null |
- **Description: Allow to inject boto3 client for Cross account access type of scenarios in using Sagemaker Endpoint **
- **Issue:#10634 #10184**
- **Dependencies: None**
- **Tag maintainer:**
- **Twitter handle:lethargicoder**
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PR_kwDOIPDwls5aiBjq
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mention how-to in LCEL index
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[] | null | 1 | 2023-09-18T06:01:30 | 2023-09-18T06:01:48 | 2023-09-18T06:01:47 |
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PR_kwDOIPDwls5aiA8f
| 10,726 |
Add async sql record manager and async indexing API
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CONTRIBUTOR
| null |
- **Description:** Add support for a SQLRecordManager in async environments. It includes the creation of `RecorManagerAsync` abstract class.
- **Issue:** None
- **Dependencies:** Optional `aiosqlite`.
- **Tag maintainer:** @nfcampos
- **Twitter handle:** @jvelezmagic
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PR_kwDOIPDwls5ahsSi
| 10,724 |
Make anthropic_api_key a secret str
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CONTRIBUTOR
| null |
This PR makes `ChatAnthropic.anthropic_api_key` a `pydantic.SecretStr` to avoid inadvertently exposing API keys when the `ChatAnthropic` object is represented as a str.
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Issue: unexpected keyword argument 'stop'
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[] | null | 8 | 2023-09-18T04:03:36 | 2024-01-05T10:48:25 | 2023-09-18T06:59:47 |
NONE
| null |
### Issue you'd like to raise.
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "D:\Users\DHANKA01\vox-usecase-wrdm-nco-dev\src\backend\api.py", line 49, in nco_generate
result=final_run(Paths.prompt_lib,Paths.sdpk_path)
File "D:\Users\DHANKA01\vox-usecase-wrdm-nco-dev\src\backend\llm_run.py", line 126, in final_run
answer_resp,content_resp,question_covered=file_run(prompt_idx,index_temp,LLM_MODEL,EMBEDDING_MODEL,max_tokens,question_covered)
File "D:\Users\DHANKA01\vox-usecase-wrdm-nco-dev\src\backend\llm_run.py", line 71, in file_run
Ans_line, res_text=get_queryAnswer(llm_model,embedding_model,index_name,max_tokens,query,flag)
File "D:\Users\DHANKA01\vox-usecase-wrdm-nco-dev\src\backend\llm_run.py", line 43, in get_queryAnswer
output1=chain.run(question)
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\chains\base.py", line 290, in run
return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\chains\base.py", line 166, in __call__
raise e
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\chains\base.py", line 160, in __call__
self._call(inputs, run_manager=run_manager)
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\chains\llm.py", line 92, in _call
response = self.generate([inputs], run_manager=run_manager)
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\chains\llm.py", line 102, in generate return self.llm.generate_prompt(
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\llms\base.py", line 141, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\llms\base.py", line 227, in generate
output = self._generate_helper(
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\llms\base.py", line 178, in _generate_helper
raise e
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\llms\base.py", line 165, in _generate_helper
self._generate(
File "D:\Users\DHANKA01\anaconda3\envs\pls_final\lib\site-packages\langchain\llms\base.py", line 527, in _generate else self._call(prompt, stop=stop, **kwargs)
TypeError: _call() got an unexpected keyword argument 'stop'
### Suggestion:
hi,
while running the same code with jupyter notebook, it's running fine but after integration with fastapi getting error.
from langchain.chains import LLMChain
chain=LLMChain(llm=llm,prompt=prompt)
chain.run(query)
Fastapi version=0.103.1
langchain version tried= 0.0.292,0.0.267,0.0.215
please suggest a fix for the same.
|
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| 1,900,117,054 |
PR_kwDOIPDwls5ahpO8
| 10,722 |
Update LangSmith Version
|
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[] | null | 1 | 2023-09-18T03:54:02 | 2023-09-18T14:12:49 | 2023-09-18T14:12:48 |
COLLABORATOR
| null |
And assign dataset ID upon project creation
|
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| 1,900,116,688 |
I_kwDOIPDwls5xQXrQ
| 10,721 |
How to custom prompt in agent type CHAT_CONVERSATIONAL_REACT_DESCRIPTION
|
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[] | null | 5 | 2023-09-18T03:53:27 | 2024-01-08T23:13:35 | null |
NONE
| null |
```
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
verbose=True,
return_intermediate_steps=True,
handle_parsing_errors=True,
memory=memory
)
```
how to overwrite the PREFIX, SUFFIX and FORMAT_INSTRUCTIONS when I using the CHAT_CONVERSATIONAL_REACT_DESCRIPTION and creating agent by ```initialize_agent``` function?
|
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PR_kwDOIPDwls5ahn4W
| 10,720 |
feat(vectorstore): :sparkles: Create new integration with PGVector async
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CONTRIBUTOR
| null |
- **Description:** Add PGVector integration with support for async connections.
- **Issue:** None
- **Dependencies:** Optionally, `asyncpg` and `greenlet`.
- **Tag maintainer:** @nfcampos
- **Twitter handle:** @jvelezmagic
Note:
I added `adelete` method to the `VectorStore` base class. It would be useful when integrating with the `SQLRecordManager`. It shouldn't have any impact on preexisting vectorstores.
|
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I_kwDOIPDwls5xQT1n
| 10,719 |
using the OpenAPI toolkit, override the headers for each request
|
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[] | null | 4 | 2023-09-18T03:30:15 | 2023-12-25T16:02:10 | null |
NONE
| null |
### Issue you'd like to raise.
I configured it to use the OpenAPI toolkit by merging various APIs, but I could not solve the problem of having to redefine the header at the request stage.
### Suggestion:
I'm not sure if there is a way to solve this in langchain or if I need to apply a separate module such as a request interceptor.
Thank you in advance for your reply :)
|
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runnable bind how to nit
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PR_kwDOIPDwls5ahVTV
| 10,717 |
Runnable fallbacks howto
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[] | null | 1 | 2023-09-18T01:35:56 | 2023-09-18T01:50:09 | 2023-09-18T01:50:09 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5ahTuJ
| 10,716 |
Runnable binding how to
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[] | null | 1 | 2023-09-18T01:25:22 | 2023-09-18T01:49:17 | 2023-09-18T01:49:16 |
COLLABORATOR
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PR_kwDOIPDwls5ahIJh
| 10,715 |
add runnable map how to
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[] | null | 1 | 2023-09-17T23:33:44 | 2023-09-17T23:49:46 | 2023-09-17T23:49:45 |
COLLABORATOR
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| 1,899,959,947 |
I_kwDOIPDwls5xPxaL
| 10,714 |
Custom Tool Return Direct BUT only if it doesnt raise a Tool Exception
|
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[] | null | 3 | 2023-09-17T23:22:13 | 2023-11-24T18:51:48 | null |
NONE
| null |
### System Info
0.0.287 langchain
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Currently I have a Custom Tool like this:
```
def _handle_error(error: ToolException) -> str:
return (
"The following errors occurred during tool execution:"
+ error.args[0]
+ "please try another tool."
)
tools = [
Tool.from_function(
name="GetCurrentWeather",
func=get_current_weather,
description="useful for getting the current weather for a location",
return_direct=True,
handle_tool_error=_handle_error)]
```
It works unless I add the return_direct=True.
When I do return_direct= True instead of trying other tools it just returns this:
```
> Entering new AgentExecutor chain...
16 AgentAction --> Thought: The question is asking about the current weather in Uludağ.
17 Action: GetCurrentWeather
18 Action Input: Uludağ
19 Thought: The question is asking about the current weather in Uludağ.
20 Action: GetCurrentWeather
21 Action Input: Uludağ
22 Observation:The following errors occurred during tool execution:the location is not found. please try another tool.
23 > Finished chain.
24 The following errors occurred during tool execution:location is not found.please try another tool.
```
### Expected behavior
Let's see the same thing without the `return_direct=True`:
```
> Entering new AgentExecutor chain...
Thought: The question is asking about the current weather in Uludağ.
Action: GetCurrentWeather
Action Input: Uludağ
Observation:The following errors occurred during tool execution:location is not found. please try another tool.
I couldn't find the current weather for Uludağ. I should try another tool.
Action: DuckDuckSearch
Action Input: "Uludağ current weather"
Observation:Current Weather. 7:35 PM. 78° F. RealFeel® 74°. RealFeel Shade™ 74°. Air Quality Fair. Wind W 14 mph. Wind Gusts 24 mph. Sunny More Details. 7:14. —. This table gives the weather forecast for Uludag at the specific elevation of 2543 m. Our advanced weather models allow us to provide distinct weather forecasts for several elevations of Uludag. ..
```
So it goes for the other tools. It should be like this for return_direct.
It should return directly only if it doesn't get an error.
|
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| 1,899,954,386 |
PR_kwDOIPDwls5ahGZ2
| 10,713 |
Mv LCEL routing doc
|
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[] | null | 1 | 2023-09-17T23:04:45 | 2023-09-17T23:33:32 | 2023-09-17T23:33:31 |
COLLABORATOR
| null |
Move to how-to
|
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