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https://api.github.com/repos/langchain-ai/langchain/issues/12585
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PR_kwDOIPDwls5eKdTu
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Install and use `ruff format` instead of black for code formatting.
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COLLABORATOR
| null |
Best to review one commit at a time, since two of the commits are 100% autogenerated changes from running `ruff format`:
- Install and use `ruff format` instead of black for code formatting.
- Output of `ruff format .` in the `langchain` package.
- Use `ruff format` in experimental package.
- Format changes in experimental package by `ruff format`.
- Manual formatting fixes to make `ruff .` pass.
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Added env vars section to missing template readmes
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PR_kwDOIPDwls5eKBy3
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Upgrade to `actions/checkout@v4` in the docs lint job.
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PR_kwDOIPDwls5eJ_b2
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add routing by embedding doc
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PR_kwDOIPDwls5eJ4Bq
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fixed _load_prompt_from_file file check
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NONE
| null |
**Description:** Fixed security issue in `_load_prompt_from_file` function(s). By passing instance of `class NotStrLike(os.PathLike)`, users are able to execute arbitrary code. `file` parameter should be either `str` or `Path` instance, and nothing else.
PoC: https://github.com/tangledgroup/langchain-prompt-exploit/blob/01795ccde3bf176e148e688079af6f6c93a730c3/1_langchain_prompt_wikipedia/main.py#L67
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PR_kwDOIPDwls5eJ306
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Test-publish to test PyPI and separate jobs to limit permissions.
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COLLABORATOR
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Before making a new `langchain` release, we want to test that everything works as expected. This PR lets us publish `langchain` to test PyPI, then install it from there and run checks to ensure everything works normally before publishing it "for real".
It also takes the opportunity to refactor the build process, splitting up the build, release-creation, and PyPI upload steps into separate jobs that do not share their elevated permissions with each other.
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I_kwDOIPDwls51XGNG
| 12,577 |
Issue: Encoding error with UnstructuredFileLoader
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[] | null | 2 | 2023-10-30T18:35:24 | 2023-10-30T19:42:34 | 2023-10-30T19:39:19 |
NONE
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Hi! Usually I process English documents, but I recently had to process files in Portuguese and UnstructuredFileLoader can't deal with it. I loose characters such as ç, é, í, ã, õ, etc. It doesn't matter if its PDF, HTML or DOCX. Is there a way to encode it properly?
ex:
Art. 110. Sem prejuzo da nova redao dada aos arts. 380 e 381 da Lei n 16.050, de 31 de julho de 2014, as outras disposies previstas nesta Lei sero aplicadas apenas aos processos de licenciamento de obras e edificaes protocolados aps a data de publicao desta Lei, exceto nos casos de manifestao formal do interessado, a qualquer tempo, optando pela anlise integral de acordo com as novas disposies. Art. 111. O 4 do art. 382 da Lei n 16.050, de 2014, passa a vigorar com a seguinte redao: Art. 382. ................................................................................... .................................................................................................... 4 Para as Operaes Urbanas Consorciadas preexistentes em vigor, as regras de finalizao devero ser definidas por lei especfica e as regras de transio para finalizao devero ser iniciadas apenas para lotes que no se enquadrem no tamanho mnimo estabelecido pela OUC, quando a Operao Urbana Consorciada atingir o limite de estoque de rea Adicional disponvel de 5% do total previsto em sua lei especfica. (NR) Art. 112. A reviso da Lei Municipal de Parcelamento, Uso e Ocupao LPUOS, Lei n 16.402, de 2016, poder rever os limites das reas de ZEU, ZEUa, ZEUP, ZEUPa, ZEM e ZEMP, com base em parmetros que considerem o disposto no art. 77 da Lei n 16.050, de 2014.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5eJ1P8
| 12,576 |
Formatting on ntbks
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I_kwDOIPDwls51W8T7
| 12,575 |
[Bing Search Wrapper] KeyError Exception 'webPages' in LangChain Bing Search Tool: Recurring Bug Impacting Search Results
|
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NONE
| null |
### System Info
Langchain version: v0.0.326
Python: 3.10
### Who can help?
@hw We have been encountering a recurring issue with the Bing Search tool (BingSearchAPIWrapper) in LangChain developed in Python. The tool occasionally fails to return results, and the backend function displays the message `webPages`. This issue is connected to the BingSearchAPIWrapper
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
#### Steps to Reproduce:
1. Query the Bing Search tool with a specific text : `{"keywords":["Everyday C%23", "advanced"]} site:https:learn.microsoft.com'.`
2. Observe that the tool fails to return results and instead displays the message `webPages`.
#### Actual Behavior:
The Bing API fails to return relevant results if there are no matches. In these situations, the API omits the `webPages` property from the response.
### Expected behavior
#### Expected Behavior:
The Bing Search tool should return relevant results for any given keyword or a message that the results can not be found.
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PR_kwDOIPDwls5eJnHQ
| 12,573 |
Minor template cleaning
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PR_kwDOIPDwls5eJgRa
| 12,572 |
feat: Add Google Cloud Text-to-Speech Tool
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[] | null | 2 | 2023-10-30T17:32:53 | 2023-10-30T21:21:27 | 2023-10-30T21:05:39 |
CONTRIBUTOR
| null |
- Add Tool for [Google Cloud Text-to-Speech](https://cloud.google.com/text-to-speech)
- Follows similar structure to [Eleven Labs Text2Speech](https://python.langchain.com/docs/integrations/tools/eleven_labs_tts)
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I_kwDOIPDwls51WpQI
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Issue: How to convert ConversationSummaryBufferMemory messages to list of tuples of strings
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### Issue you'd like to raise.
I have a ConversationSummaryBufferMemory object which stores the chat history between human and AI. One such history looks like
```python
[HumanMessage(content='Hi', additional_kwargs={}, example=False),
AIMessage(content='Hi, how can I help you?', additional_kwargs={}, example=False),
HumanMessage(content='What is TPA', additional_kwargs={}, example=False),
AIMessage(content='TPA stands for TPA', additional_kwargs={}, example=False)]
```
I am looking a list of tuples of messages like
```[('Hi', 'Hi, how can I help you?'), ('What is TPA','TPA stands for TPA')]```
How can I do that?
### Suggestion:
_No response_
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I_kwDOIPDwls51WiRa
| 12,570 |
After doing merge_from with Faiss, vector store appears as None.
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[] | null | 1 | 2023-10-30T17:06:45 | 2023-11-07T19:32:22 | 2023-11-01T18:52:29 |
NONE
| null |
### Issue with current documentation:
I have a faiss vector store that I created before, when I print it it appears as <langchain.vectorstores.faiss.FAISS object at 0x0000022B3DFBBB50>. Then, I create a new faiss vector store and when I print it, it appears as <langchain.vectorstores.faiss.FAISS object at 0x0000022B7D0F4810>. I want to merge these two faiss objects, so I tried the merge_from method. However, when I try merge_from, vector store appears None. Here is my code:
vector_store = FAISS.from_texts(text_chunks, embeddings,
metadatas=[{"source": f"{created_pkl_name}:{i}"} for i in
range(len(text_chunks))])
print("vector_store_new: ",vector_store) # <langchain.vectorstores.faiss.FAISS object at 0x0000022B7D0F4810>
if os.path.exists(faiss_path_all):
with open(faiss_path_all, 'rb') as faiss_file_all:
all_data = pickle.load(faiss_file_all)
print("all_data: ", all_data) # <langchain.vectorstores.faiss.FAISS object at 0x0000022B3DFBBB50>
all_data = all_data.merge_from(vector_store)
print(all_data) # None
How I can solve this problem? Is there another way for merge two faiss vector stores?
### Idea or request for content:
From the example (https://python.langchain.com/docs/integrations/vectorstores/faiss?highlight=FAISS#merging) I tried the same way, but still it doesn't work for me.
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PR_kwDOIPDwls5eJU51
| 12,569 |
bump 326
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[] | null | 1 | 2023-10-30T17:03:48 | 2023-10-30T17:11:18 | 2023-10-30T17:11:17 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5eI0Hi
| 12,565 |
Fix sources in LlamaIndexRetriever #12563
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[] | null | 2 | 2023-10-30T15:47:05 | 2023-11-02T09:57:37 | null |
NONE
| null |
Description: Fix sources in LlamaIndexRetriever
Issue: https://github.com/langchain-ai/langchain/issues/12563
I was facing issues with sources like it was not working on Latest llamaindex
Then I noticed this and tested it worked fine for me
So, I like to submit the PR
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I_kwDOIPDwls51Vx3s
| 12,564 |
Feature: Addition of HuggingFaceTextGenInferenceAuto for Automated Container and Server Management
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[] | null | 2 | 2023-10-30T15:26:28 | 2023-10-30T20:36:04 | null |
CONTRIBUTOR
| null |
### Update: Pull request here: #12586
Video Demo: [https://youtu.be/wspB6QbDzBc](https://youtu.be/wspB6QbDzBc)
### Feature request
Description:
I have developed a class `HuggingFaceTextGenInferenceAuto` to automate the process of managing Docker inference server containers for the `HuggingFaceTextGenerationInference` LLM. It essentially acts as a factory to create an instance of `HuggingFaceTextGenInference` via the `from_docker` classmethod. This class can create, reuse, or recreate containers as-needed based on parameter changes.
The objective is to simplify the management of HuggingFace TextGen inference servers, and allow users to spin up Docker containers for model inference with ease. The code also contains methods to handle the health checks, wait for model downloads, and ensure container and API readiness before proceeding.
Here is a breakdown of the main functionalities:
**Automated Docker Container Management:**
* Create, reuse, or recreate Docker containers for HuggingFace TextGen inference.
* Auto-increment host port if the specified port is unavailable.
* Provide a health check and wait for model download completion.
**Parameter Management:**
* Handle Docker parameters for textgen-inference-server and HuggingFaceTextGenInference parameters with Pydantic models.
* Provide methods to convert parameters to dictionaries for easier management.
Validate parameters and create new containers based on parameter diffs
**Utility Methods:**
* Methods to get the model name, image tag, container, etc., from various inputs.
* Utility methods for converting shared memory size, checking port availability, and finding available host ports.
The code is ready and I plan to submit a pull request for review and integration. I believe this feature will be a valuable addition to the Langchain library, allowing for easier rapid prototyping of LLM-powered applications.
### Motivation
1. **Reduce Manual Intervention:**
- Minimize the steps a user has to take to spin up a HuggingFace TextGen Inference server, making the process faster and less error-prone.
- By automated handling of health checks, waiting for model downloads, and ensuring the readiness of the container and API, we aim to expedite prototyping of LLM powered applications. (We expect this will be especially useful for things like, e.g. auto-evaluation / parameter searches for LLM apps.)
2. **Ensure Consistency:**
- Provide a structured way to handle parameters and configurations for both container and Langchain LLM wrapper
### Your contribution
The `HuggingFaceTextGenInferenceAuto` which facilitates the above goals.
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I_kwDOIPDwls51Vsuq
| 12,563 |
while importing LlamaIndexRetriever, an error occurs that
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[] | null | 4 | 2023-10-30T15:17:31 | 2023-11-29T13:51:57 | null |
NONE
| null |
### System Info
from langchain.retrievers.llama_index import LlamaIndexRetriever
------
```shell
llama-index==0.8.55
langchain==0.0.325
```
> in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
503 if len(args) != 1:
504 raise ValueError("`run` supports only one positional argument.")
--> 505 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
506 _output_key
507 ]
509 if kwargs and not args:
510 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
511 _output_key
512 ]
in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
313 inputs, outputs, return_only_outputs
...
---> 32 response = index.query(query, response_mode="no_text", **self.query_kwargs)
33 response = cast(Response, response)
34 # parse source nodes
AttributeError: 'VectorStoreIndex' object has no attribute 'query'
Attribute "query" is a deprecated method. Therefore, it needs to be updated.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from llama_index import VectorStoreIndex
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.retrievers.llama_index import LlamaIndexRetriever
index = VectorStoreIndex.from_documents(**hoge)
retriever = LlamaIndexRetriever(index = index)
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff",retriever=LlamaIndexRetriever(index=index))
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
```
### Expected behavior
Successfully read llama index retriever
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PR_kwDOIPDwls5eIgRb
| 12,561 |
Image Caption accepts bytes for images
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[] | null | 1 | 2023-10-30T15:05:31 | 2023-10-30T16:29:55 | 2023-10-30T16:29:54 |
COLLABORATOR
| null |
Accept bytes for images in image caption
|
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https://api.github.com/repos/langchain-ai/langchain/issues/12560
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| 1,968,589,077 |
I_kwDOIPDwls51VkkV
| 12,560 |
chat_history conflict when using ConversationalRetrievalChain.from_llm
|
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[] | null | 10 | 2023-10-30T15:01:25 | 2023-11-03T09:00:42 | null |
NONE
| null |
### System Info
System Info:
langchain==0.0.325
Python 3.11.4
Flask==3.0.0
Flask-Cors==4.0.0
Flask-RESTful==0.3.10
Case 1:
```
dbqa = ConversationalRetrievalChain.from_llm(llm, vectordb.as_retriever(), memory=memory)
response = dbqa({"question": data["query"]})
```
Error:
Missing some input keys: {'chat_history'}
Call Stack:
`Chain.__call__()`
`Chain.prepare_inputs(inputs)` called from `__call__()` **line 286** with `inputs={'question': 'hey bot!'}`
`if self.memory is not None:` in `prepare_inputs()` passed
`external_context = self.memory.load_memory_variables(inputs)` sets `external_context` to `{'DocScanner.pdf': []}`
`self._validate_inputs(inputs)` on **line 443**
`missing_keys = set(self.input_keys).difference(inputs)` results in `missing_keys={'chat_history'}`
Case 2:
```
dbqa = ConversationalRetrievalChain.from_llm(llm, vectordb.as_retriever(), memory=memory)
response = dbqa({"question": data["query"], "chat_history": chat_history})
```
Error:
ValueError: One input key expected got ['chat_history', 'question']
Call Stack:
`Chain.__call__()`
`Chain.prepare_inputs(inputs)` called from `__call__()` **line 286** with `inputs={'question': 'hey bot!'}`
`if self.memory is not None:` in `prepare_inputs()` passed
`external_context = self.memory.load_memory_variables(inputs)` sets `external_context` to `{'DocScanner.pdf': []}`
`self._validate_inputs(inputs)` on **line 443**
`missing_keys = set(self.input_keys).difference(inputs)` results in `missing_keys={}`
`Chiain.__call__` line 303 sets `outputs={'answer': " Of course! I...elp today?'}`
`self.prep_outputs` called on line 312.
inside `prepare_outputs`, `if self.memory is not None:` check passes
`self.memory.save_context(inputs, outputs)` calls to `BaseChatMemory.save_context`
`input_str, output_str = self._get_input_output(inputs, outputs)` calls to `BaseChatMemory._get_input_output`
`if self.input_key is None:` check passes
call to `langchain.memory.utils.get_prompt_input_key`
`prompt_input_keys = list(set(inputs).difference(memory_variables + ["stop"]))` returns `['chat_history', 'question']`
`if len(prompt_input_keys) != 1:` check passes raising the error
### Who can help?
Anyone
### 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
steps to reproduce:
1. clone [repo](https://github.com/afif-malghani/LangChain-papers)
2. create a virtual environment with `python3 -m venv env`
3. activate the virtual environment with `source env/bin/activate`
4. install all packages with `pip install -r requirements`
5. run flask server with `flask run --debug`
6. upload a pdf at the home pate of the application displayed at `http://localhost:5000/`
7. In the Chat section enter the message you want to send to the bot.
### Expected behavior
Response should be returned either with or without `chat_history`.
Currently it is not returned in either of the cases.
Edit: upgraded to langchain 0.0.325, issue persists.
Edit 2: improved readability
|
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reopened
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https://api.github.com/repos/langchain-ai/langchain/issues/12559
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https://github.com/langchain-ai/langchain/pull/12559
| 1,968,585,845 |
PR_kwDOIPDwls5eIdu3
| 12,559 |
Accepts bytes in image caption loader
|
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closed
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[] | null | 2 | 2023-10-30T15:00:11 | 2023-11-15T21:13:31 | 2023-10-30T15:05:43 |
COLLABORATOR
| null |
- Update image_captions.py
- x
|
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| 1,968,584,262 |
PR_kwDOIPDwls5eIdYx
| 12,558 |
Allow astream_log to be used inside atrace_as_chain_group
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[] | null | 1 | 2023-10-30T14:59:40 | 2023-10-30T15:55:17 | 2023-10-30T15:55:16 |
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/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls51VSvx
| 12,557 |
AttributeError: 'Engine' object has no attribute '_instantiate_plugins', while trying to connect with MS SQL
|
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[] | null | 1 | 2023-10-30T14:31:50 | 2023-10-30T14:47:33 | null |
NONE
| null |
### System Info
latest version of langchain
python=3.11
sqlalchemy==1.3.17
### Who can help?
from sqlalchemy import create_engine
import os, sys, openai
import constants, definitions
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
from langchain.chat_models import ChatOpenAI
from urllib.parse import quote_plus
driver = 'ODBC Driver 17 for SQL Server'
host = '******'
user = '*****'
database = '*****'
password = '*******'
encoded_password = quote_plus(password)
# db = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{encoded_password}@{host}/{database}?driver={quote_plus(driver)}" #, include_tables = ['HRMSGPTAutomation'], sample_rows_in_table_info=2)
connection_uri = (f"mssql+pyodbc://{user}:{encoded_password}@{host}/{database}?driver={quote_plus(driver)}")
engine = create_engine(connection_uri)
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True)
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from sqlalchemy import create_engine
import os, sys, openai
import constants, definitions
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
from langchain.chat_models import ChatOpenAI
from urllib.parse import quote_plus
driver = 'ODBC Driver 17 for SQL Server'
host = '******'
user = '*****'
database = '*****'
password = '*******'
encoded_password = quote_plus(password)
# db = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{encoded_password}@{host}/{database}?driver={quote_plus(driver)}" #, include_tables = ['HRMSGPTAutomation'], sample_rows_in_table_info=2)
connection_uri = (f"mssql+pyodbc://{user}:{encoded_password}@{host}/{database}?driver={quote_plus(driver)}")
engine = create_engine(connection_uri)
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True)
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
### Expected behavior
how to solve the error
|
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PR_kwDOIPDwls5eIABw
| 12,556 |
Fix sources in RetrievalQAWithSourcesChain
|
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[] | null | 3 | 2023-10-30T14:02:23 | 2023-12-12T16:25:55 | null |
NONE
| null |
- **Description:** Fix sources in RetrievalQAWithSourcesChain
- **Issue:** #10575 #11387 #9774
- **Twitter handle:** Syed_Mujeeb_H
I was facing issues with sources like it was not coming in chain response
Then I noticed this [PR](https://github.com/langchain-ai/langchain/pull/11032) and tested it worked fine for me
So, I like to submit the PR
|
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PR_kwDOIPDwls5eH7-0
| 12,555 |
[Matching Engine] Update the Matching Engine to include the distance and filters
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[] | null | 2 | 2023-10-30T13:56:34 | 2023-10-30T21:12:59 | 2023-10-30T21:12:59 |
CONTRIBUTOR
| null |
Hello 👋,
This Pull Request adds more capability to the [MatchingEngine](https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html) vectorstore of GCP. It includes the `similarity_search_by_vector_with_relevance_scores` function and also [filters](https://cloud.google.com/vertex-ai/docs/vector-search/filtering) to `filter` the namespaces when retrieving the results.
- **Description:** Add [filter](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.MatchingEngineIndexEndpoint#google_cloud_aiplatform_MatchingEngineIndexEndpoint_find_neighbors) in `similarity_search` and add `similarity_search_by_vector_with_relevance_scores` method
- **Dependencies:** None
- **Tag maintainer:** Unknown
Thank you!
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PR_kwDOIPDwls5eHoCF
| 12,554 |
Issue: fix the issue #11648 init minimax llm
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[] | null | 4 | 2023-10-30T13:19:26 | 2023-11-20T06:30:16 | 2023-10-30T21:30:18 |
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),
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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:
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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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Fix Issue https://github.com/langchain-ai/langchain/issues/11648 Minimax llm failed to initialize
The idea of this fix is https://github.com/langchain-ai/langchain/issues/10917#issuecomment-1765606725
do not use underscore in python model class
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I_kwDOIPDwls51UhC0
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Neo4J keyword index creation problem
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[] | null | 2 | 2023-10-30T13:04:58 | 2023-10-30T13:16:45 | null |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.320
### Who can help?
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When you try the tutorial:
```
index_name = "vector" # default index name
keyword_index_name = "text_index" # default keyword index name
store = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name=index_name,
keyword_index_name=keyword_index_name,
search_type="hybrid",
)
```
It produces an error because in fact the keyword index is not created yet:
`ValueError: The specified keyword index name does not exist. Make sure to check if you spelled it correctly`
So I thought I should maybe call this first:
`store.create_new_keyword_index(text_node_properties=["text"])`
However the function is not enabling me to choose the index name and it defaults to "None" string.
I suggest the last method should include the name of the index.
### Expected behavior
The method should auto create the keyword index on the text properties.
|
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PR_kwDOIPDwls5eHfcH
| 12,552 |
Add message to documents
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[] | null | 1 | 2023-10-30T12:59:46 | 2023-11-09T15:30:49 | 2023-11-09T15:30:48 |
CONTRIBUTOR
| null |
This adds the response message as a document to the rag retriever so users can choose to use this. Also drops document limit.
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Issue: <Using RAG over code, how to split vue and json source>
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[] | null | 1 | 2023-10-30T12:55:56 | 2023-10-30T13:18:00 | null |
NONE
| null |
### Issue you'd like to raise.
https://python.langchain.com/docs/use_cases/question_answering/code_understanding
When using RAG over .vue、.json code sources, how to split them then save to vector storage?
### Suggestion:
_No response_
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[OpenSearch Vector Search] Add Method for approximate_search_query_match_text_with_boolean_filter
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[] | null | 4 | 2023-10-30T12:47:57 | 2023-10-31T05:35:14 | null |
NONE
| null |
Replace this entire comment with:
- **Description:**
I added a function '_approximate_search_query_match_text_with_boolean_filter' at opensearch_vector_search.py.
- **Issue:**
To improve search performance of OpenSearch vector search, it should be possible to compare the similarity with the vector embedding as well as with the text field. I'm developing Chatbot with Langchain based on Opensearch index as a vectorDB now and it helped to improve the search performance.
- **Dependencies:** any dependencies required for this change -> NO,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
|
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| 1,968,267,173 |
PR_kwDOIPDwls5eHXnA
| 12,549 |
Add Runnable.with_listeners()
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[] | null | 1 | 2023-10-30T12:43:03 | 2023-10-31T11:04:53 | 2023-10-31T11:04:51 |
COLLABORATOR
| null |
- This binds start/end/error listeners to a runnable, which will be called with the Run object
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I_kwDOIPDwls51TfFX
| 12,547 |
Custom LLM Implementation
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[] | null | 7 | 2023-10-30T10:44:33 | 2023-11-08T07:09:08 | null |
CONTRIBUTOR
| null |
### Feature request
Integrate other models from hugging face models models (likely decoder based models) as a LLM , which we can use for RAG applications
### Motivation
Since the model weights of LLM are huge we can have our own model trained on small dataset they can be pluged into RAG applications
### Your contribution
need to think of it actually how can i able to contribute to this
|
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I_kwDOIPDwls51TZcl
| 12,546 |
LCEL cookbook issue : Error: unsupported operand type(s) for |: 'ChatPromptTemplate' and 'ChatOpenAI'
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[] | null | 2 | 2023-10-30T10:32:14 | 2023-10-30T11:03:24 | 2023-10-30T11:03:23 |
NONE
| null |
### System Info
python =3.9.14
langchain =0.0.325
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
go to the first LLM + prompt cookbok for LCEL and run it in a local env , you will get an error at this part
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
prompt = ChatPromptTemplate.from_template("tell me a joke about {foo}")
model = ChatOpenAI()
chain = prompt | model
Error: unsupported operand type(s) for |: 'ChatPromptTemplate' and 'ChatOpenAI'
### Expected behavior
It should have run without and errors
|
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PR_kwDOIPDwls5eGhC_
| 12,545 |
Raise ValidationError when type in the messages are wrong
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[] | null | 1 | 2023-10-30T10:32:05 | 2023-11-13T03:16:55 | null |
NONE
| null |
Even in the case when messages are list, there can be some elements in the list that can be non-BaseMessage type. So have to raise ValidationError from Pydantic Library for easy check.
Originally, it just print out the information from Pydantic and it is so hard to reason why my code is wrong.
<!-- 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/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5eGYhs
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Add IBM watsonx.ai as a new LLM provider (WatsonxLLM)
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CONTRIBUTOR
| null |
- **Description:** added IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) as a new LLM provider (`WatsonxLLM`)
- **Dependencies:** [ibm-watson-machine-learning](https://pypi.org/project/ibm-watson-machine-learning/),
- **Tag maintainer:** @baskaryan, @eyurtsev ,
- **Twitter handle:** details in comment below.
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. ✅
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access ❌ ,
2. an example notebook showing its use. It lives in `docs/extras` directory. ❌ => `docs/docs/integrations/llms/watsonxllm.ipynb`
I'm also not sure what the procedure is for integration tests. I have tested locally with my api key.
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PR_kwDOIPDwls5eGQAX
| 12,542 |
Mask API Key for OpenAI based ChatModels (OpenAI,AzureOpenAi,Konko)
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NONE
| null |
- **Description:** Added API Key masking for all OpenAI based ChatModels (OpenAI, Azure OpenAI, Konko)
- Updated OpenAI Chat model to use SecretStr to mask sensitive API Key. (Used convert_to_secret_str recently added to util)
- Added unit tests for all modified Chat models.
- **Issue:** #12165
- **Dependencies:** None
- **Tag maintainer:** @eyurtsev
Konko and OpenAI both implemented OpenAI, so needed to change all three to avoid errors.
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I_kwDOIPDwls51S-WU
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couldn't work from langchain,OpenAI and Milvus
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[] | null | 1 | 2023-10-30T09:32:59 | 2023-10-30T09:46:51 | null |
NONE
| null |
### System Info
Milvus standlone:2.3.2
Langchain version:0.0.325
OpenAI: 0.28.1
pymilvus: 2.3.2
Pydantic:2.4.2
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
this is my testing coding
import os
import openai
import langchain
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chat_models import AzureChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.vectorstores import Milvus
from langchain.document_loaders import WebBaseLoader
langchain.debug = True
MILVUS_HOST = "10.224.122.39"
MILVUS_PORT = "19530"
#open AI parameters setting
api_base = "https://model-serving.us-east-2.int.infra.intelligence.webex.com/azure/v1"
api_key = "*******"
api_version = "2023-03-15-preview"
api_type = "azure_ad"
api_temperature = "0"
openai.api_type = api_type
openai.api_version = api_version
openai.api_base = api_base
openai.api_key = api_key
os.environ["OPENAI_API_KEY"] = api_key
os.environ["OPENAI_API_BASE"] = api_base
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def insert_vector2DB():
text_splitter_chunk_size = 2000
text_splitter_chunk_overlap = 200
loader = WebBaseLoader([
"https://milvus.io/docs/overview.md",
"https://help.webex.com/en-us/article/nk1shtj/Get-started-with-Webex-Calling",
"https://help.webex.com/en-us/article/nq28lbi/Get-Started-with-Your-New-Cisco-IP-DECT-6800-Series",
"https://help.webex.com/en-us/article/n62735y/Webex-%7C-Record-a-meeting-or-webinar",
])
documents = loader.load()
for doc in documents:
doc.page_content = doc.page_content
doc.metadata = {"url": doc.metadata["source"]}
text_splitter = RecursiveCharacterTextSplitter(chunk_size=text_splitter_chunk_size, chunk_overlap=text_splitter_chunk_overlap)
texts = text_splitter.split_documents(documents)
# split documents
#text_splitter = SentenceTransformersTokenTextSplitter(chunk_size=2000, chunk_overlap=200)
#text_splitter = CharacterTextSplitter(chunk_size=2000, chunk_overlap= 200)
#docs = text_splitter.split_documents(documents)
#print(documents)
# embeddings with Open AI
embeddings = OpenAIEmbeddings(
deployment="gpt-35-turbo",
openai_api_key=openai.api_key,
openai_api_base=openai.api_base,
openai_api_version=openai.api_version,
openai_api_type=openai.api_type,
chunk_size=16)
# call openai wrapper class,work-able with opensource embeddings
#embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L12-v2")
#print(embeddings)
# insert vector into milvus.
Milvus.from_documents(
texts,
embedding=embeddings,
collection_name='milvus_help',
drop_old=True,
connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT}
)
def query(question):
#1.connect the vector Database
#embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L12-v2")
embeddings = OpenAIEmbeddings(
deployment="gpt-35-turbo",
openai_api_key=openai.api_key,
openai_api_base=openai.api_base,
openai_api_version=openai.api_version,
openai_api_type=openai.api_type,
chunk_size=8)
vector_db = Milvus(
embeddings,
connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT},
collection_name='milvus_help'
)
#2.search the data
# top 20 similar vector data
retriever = vector_db.as_retriever(search_type="mmr", search_kwargs={"k": 20}).get_relevant_documents(question)
#put the retriever into LLM
# Configure system prompt
system_template = """Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
The "SOURCES" part should be a reference to the source of the document from which you got your answer.
Example of your response should be:
---
The answer is foo
SOURCES: xyz
---
Begin!
----------------
{summaries}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}
# Create an AzureChatOpenAI llm
llm = AzureChatOpenAI(
temperature=api_temperature,
openai_api_key=openai.api_key,
openai_api_base=openai.api_base,
openai_api_version=openai.api_version,
openai_api_type=openai.api_type,
deployment_name="gpt-35-turbo")
# Create a chain that uses the Chroma vector store
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(
llm=llm,
chain_type="map_reduce",
retriever=vector_db.as_retriever(),
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs
)
print(qa_chain)
#response = qa_chain.run({"question": question})
result = qa_chain(
{
"question": question,
},
return_only_outputs=True,
)
response = f"Answer:\t{result['answer']}\n\nSources:\t{result['sources']}\n"
print(response)
def __test_query():
query("What is IP DECT 6800?")
query("What is Webex Calling?")
query("What is Milvus?")
def __test_init_db():
insert_vector2DB()
if __name__ == '__main__':
__test_init_db()
#__test_query()
### Expected behavior
except it can work well, but it creates an error.
Traceback (most recent call last):
File "/Users/lizhon/PycharmProjects/AITesting/helpDoc.py", line 169, in <module>
__test_init_db()
File "/Users/lizhon/PycharmProjects/AITesting/helpDoc.py", line 166, in __test_init_db
insert_vector2DB()
File "/Users/lizhon/PycharmProjects/AITesting/helpDoc.py", line 70, in insert_vector2DB
Milvus.from_documents(
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/langchain/schema/vectorstore.py", line 438, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/langchain/vectorstores/milvus.py", line 827, in from_texts
vector_db.add_texts(texts=texts, metadatas=metadatas)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/langchain/vectorstores/milvus.py", line 427, in add_texts
embeddings = self.embedding_func.embed_documents(texts)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/langchain/embeddings/openai.py", line 490, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/langchain/embeddings/openai.py", line 374, in _get_len_safe_embeddings
response = embed_with_retry(
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/langchain/embeddings/openai.py", line 107, in embed_with_retry
return _embed_with_retry(**kwargs)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/concurrent/futures/_base.py", line 437, in result
return self.__get_result()
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/concurrent/futures/_base.py", line 389, in __get_result
raise self._exception
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/langchain/embeddings/openai.py", line 104, in _embed_with_retry
response = embeddings.client.create(**kwargs)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/openai/api_resources/embedding.py", line 33, in create
response = super().create(*args, **kwargs)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 155, in create
response, _, api_key = requestor.request(
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/openai/api_requestor.py", line 299, in request
resp, got_stream = self._interpret_response(result, stream)
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/openai/api_requestor.py", line 710, in _interpret_response
self._interpret_response_line(
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/openai/api_requestor.py", line 775, in _interpret_response_line
raise self.handle_error_response(
File "/Users/lizhon/PycharmProjects/AITesting/venv/lib/python3.8/site-packages/openai/api_requestor.py", line 428, in handle_error_response
error_code=error_data.get("code"),
AttributeError: 'str' object has no attribute 'get'
Process finished with exit code 1
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PR_kwDOIPDwls5eGGpA
| 12,540 |
Update neo4j template readmes
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[] | null | 1 | 2023-10-30T09:27:15 | 2023-10-30T14:57:53 | 2023-10-30T14:57:53 |
CONTRIBUTOR
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| 1,967,821,242 |
I_kwDOIPDwls51SpG6
| 12,539 |
Bug in Prompt Description in SQLDatabaseToolkit Reproduction:
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[] | null | 1 | 2023-10-30T08:44:19 | 2023-10-30T08:46:43 | null |
NONE
| null |
### System Info
win11
python3.9.12
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain.agents import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.chat_models import ChatOpenAI
db = SQLDatabase.from_uri("sqlite:///../../../../../notebooks/Chinook.db")
toolkit = SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0))
agent_executor = create_sql_agent(
llm=OpenAI(temperature=0),
toolkit=toolkit,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)
agent_executor.run("Describe the playlisttrack table")
When running the above code, it is likely to produce an error even gpt-3.5 model is used. The reason for the error is often due to the input of the table name with single quotes '' in the call to sql_db_schema, which is caused by the prompt. In the get_tools method of SQLDatabaseToolkit, the default instance description for the info_sql_database_tool_description tool is "Example Input: 'table1, table2, table3'", which is the source of the error. The correct description should be "Example Input: table1, table2, table3". Even if a powerful model like GPT-4 is used, it may still produce an error when calling sql_db_schema, although it can correct it later. However, after the modification, some less powerful models can also correctly call sql_db_schema.
I found the same issue reported by someone else and was troubled by it. However, after searching the repo source code, I found that the description is still incorrect, so I am reporting this bug.
### Expected behavior
Modify the prompt description in the get_tools method of SQLDatabaseToolkit.
|
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| 1,967,809,475 |
I_kwDOIPDwls51SmPD
| 12,538 |
Error in Google Palm
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[] | null | 1 | 2023-10-30T08:38:48 | 2023-10-30T08:59:41 | null |
NONE
| null |
### System Info
'''
Traceback (most recent call last):
File "query.py", line 51, in <module>
action1 = act(total_text)
File "query.py", line 10, in act
return llm(text)
File "C:\Users\a_alk\anaconda3\lib\site-packages\langchain\llms\base.py", line 790, in __call__
self.generate(
IndexError: list index out of range
'''
I received this error which endicate the list generated by model is empty.
What should I do?
#### The function is this
'''
def act(text):
llm = GooglePalm(google_api_key = api_key, temperature=1, max_output_tokens=1)
return llm(text)
'''
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
def act(text):
llm = GooglePalm(google_api_key = api_key, temperature=1, max_output_tokens=1)
return llm(text)
### Expected behavior
String output
|
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| 1,967,669,939 |
PR_kwDOIPDwls5eFU7V
| 12,537 |
add docs to min_chunk_size
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[] | null | 1 | 2023-10-30T07:20:05 | 2023-10-31T15:58:41 | 2023-10-30T22:13:52 |
CONTRIBUTOR
| null |
Minor addition to documentation to elaborate on min_chunk_size.
|
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| 1,967,537,805 |
PR_kwDOIPDwls5eE4Iu
| 12,536 |
Fixing some grammatcal issues
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[] | null | 5 | 2023-10-30T05:25:07 | 2023-12-19T05:16:04 | 2023-12-19T05:14:50 |
CONTRIBUTOR
| null |
Fixing some doc issues
@eyurtsev , @baskaryan
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I_kwDOIPDwls51Rg0f
| 12,535 |
similarity_search_with_relevance_scores
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[] | null | 2 | 2023-10-30T05:11:31 | 2023-10-30T13:09:43 | null |
NONE
| null |
### System Info
The returned score is not in the range of [0,1], rather it is a relatively large negative number such as -172.597
I am only providing the query
I am using langchain with bedrock under aws
I wonder if should set something at the point of building the vectorstore or should have set the score function before using it. If so, please let me know how
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
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- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Build the vector store
use similarity_search_with_relevance_scores(query)
### Expected behavior
Return range of numbers to be between 0 and 1 but it is a negative number
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PR_kwDOIPDwls5eEpcL
| 12,534 |
goog translate nb formatting
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[] | null | 1 | 2023-10-30T04:25:16 | 2023-10-30T04:27:05 | 2023-10-30T04:27:05 |
COLLABORATOR
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| 1,967,466,491 |
I_kwDOIPDwls51RSf7
| 12,533 |
Using VectorDBQA and llama2 . Why are there other irrelevant questions and answers in addition to the answers to the questions？
|
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open
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[] | null | 2 | 2023-10-30T04:19:48 | 2023-10-30T07:21:41 | null |
NONE
| null |
### Issue you'd like to raise.
import sys, os
current_dir = os.path.dirname(os.path.abspath(__file__))
project_root = os.path.abspath(os.path.join(current_dir, os.pardir))
sys.path.append(project_root)
from langchain.document_loaders import TextLoader
from langchain.text_splitter import SpacyTextSplitter
from langchain.vectorstores import FAISS
from langchain import VectorDBQA, OpenAI
from langchain.agents import tool
from models.use import embeddings_zh, llama2
# 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)
docsearch = FAISS.from_documents(texts, embeddings_zh)
# use llama-2-7b
faq_chain = VectorDBQA.from_chain_type(
llm=llama2,
vectorstore=docsearch,
verbose=True
)
@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)
# res = faq('如何更改帐户信息')
# print('res', res)
# > Finished chain.
# res 登录您的帐户，然后点击“我的帐户”以更改个人信息、收货地址等。
# Question: 如何下订单？
# Helpful Answer: 浏览商品并将想要购买的商品加入购物车。当您准备结算时，点击购物车图标，然后选择送货地址、付款方式和物流方式。
# Question: 如何注册新帐户？
# Helpful Answer: 点击网站右上角的“注册”按钮，然后按照提示填写相关信息并设置密码。完成后，您将收到一封验证电子邮件。点击邮件中的链接以激活您的帐户。
# Question: 忘记密码怎么办？
# Helpful Answer: 点击登录页面的“忘记密码”链接，输入您的电子邮件地址。我们将向您发送一封包含重置密码链接的邮件。请点击链接并按照提示操作。
# Question: 如何更改收货地址？
# Helpful Answer: 在订单发货前，您可以登录帐户，进入“我的订单”页面，选择要修改的订单并点击“修改地址”。如果订单已发货，您需要联系客服协助处理。
# Question: 如何查询发票信息？
# Helpful Answer: 登录您的帐户，进入“我的发��
Why are there other irrelevant questions and answers in addition to the answers to the questions? What parameters do I need to set so that it only returns answers to questions?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5eDvey
| 12,532 |
update contributing
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[] | null | 1 | 2023-10-29T23:21:18 | 2023-10-29T23:22:20 | 2023-10-29T23:22:19 |
COLLABORATOR
| null | null |
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| 1,967,207,233 |
PR_kwDOIPDwls5eDvd8
| 12,531 |
Batch apply `poetry lock --no-update` for all templates
|
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[] | null | 1 | 2023-10-29T23:21:05 | 2023-10-30T22:27:41 | 2023-10-30T22:18:53 |
CONTRIBUTOR
| null |
Ran the following bash script for all templates
```bash
#!/bin/bash
set -e
current_dir="$(pwd)"
for directory in */; do
if [ -d "$directory" ]; then
(cd "$directory" && poetry lock --no-update)
fi
done
cd "$current_dir"
```
|
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| 1,967,202,600 |
PR_kwDOIPDwls5eDujC
| 12,530 |
Force List[Tuple[str,str]] to chat history widget
|
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[] | null | 1 | 2023-10-29T23:06:34 | 2023-10-30T22:31:34 | 2023-10-30T22:19:33 |
CONTRIBUTOR
| null | null |
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| 1,967,197,810 |
PR_kwDOIPDwls5eDtl5
| 12,529 |
Bagatur/fix doc ci
|
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PR_kwDOIPDwls5eDp19
| 12,528 |
improve agent templates
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template updates
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PR_kwDOIPDwls5eDoOX
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RAG template for MongoDB Atlas Vector Search
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PR_kwDOIPDwls5eDmaz
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Introducing Asynchronous Streaming in Ollama
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NONE
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** I would like to add an asynchronous streaming feature to Ollama. This enhancement significantly improves the performance and efficiency when integrated with FastAPI.
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5eDlne
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Bagatur/lakefs loader2
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PR_kwDOIPDwls5eDix3
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Harrison/pii chatbot
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[] | null | 1 | 2023-10-29T20:34:57 | 2023-10-31T01:13:13 | 2023-10-31T01:13:12 |
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the pii detection in the template is pretty basic, will need to be customized per use case
the chain it "protects" can be swapped out for any chain
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I_kwDOIPDwls51QCUq
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Trouble using the Conversational Agent: Missing some input keys: {'input'} and Agent.plan() got multiple values for argument 'intermediate_steps'
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[] | null | 8 | 2023-10-29T20:12:26 | 2023-12-08T08:54:34 | null |
NONE
| null |
### Issue you'd like to raise.
Im trying to use an agent as a router for two retrievals. I also want it to have memory so im using the conversational agent with two RetrievalQA chains and ConversationBufferWindowMemory.
Heres the code after creating the retrievals and chatTemplates:
```
chat = ChatVertexAI(
model_name = "chat-bison-32k",
temperature = 0.5,
top_p = 0.8,
top_k = 40,
max_output_tokens = 300
)
prompt1 = PromptTemplate(
input_variables=["context", "input"],
template=chatTemplate1
)
chain_type_kwargs1 = {"prompt": prompt1}
prompt2 = PromptTemplate(
input_variables=["context", "input"],
template=chatTemplate2
)
chain_type_kwargs2 = {"prompt": prompt2}
faq = RetrievalQA.from_chain_type(
llm = llm,
retriever = chroma_retriever,
chain_type="stuff",
chain_type_kwargs=chain_type_kwargs1
)
complex_products = RetrievalQA.from_chain_type(
llm = llm,
retriever = ensemble_retriever,
chain_type="stuff",
chain_type_kwargs=chain_type_kwargs2
)
tools = [
Tool(
name="FAQ about the store",
func= faq.run,
description="useful for when you need to answer questions about related to frequently asked questions. This tool provides information on business hours and store location. To use, input a well-formulated question.",
),
Tool(
name="Product information",
func=complex_products.run,
description="useful for when you need to answer questions about certain products that the costumer needs. Input should be a fully formed question.",
),
]
memory = ConversationBufferWindowMemory(memory_key="chat_history",input_key="input",output_key='output', return_messages=True, k=4)
```
Now, I've been trying two things. The first one is to call my agent with the memory directly. I tried it with and without using .run():
```
agent = initialize_agent(
tools = tools,
llm = chat,
agent = AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
max_iterations = 3,
early_stopping_method = 'generate',
memory = memory
)
result = agent("What are the closing hours?")
result["output"]
```
but with some questions, not all, i get the following error:
```
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: FAQ about the store
Action Input: What are the closing hours?
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[45], line 1
----> 1 result = agent("What are the closing hours?")
2 result["output"]
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
313 inputs, outputs, return_only_outputs
314 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:304, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
297 run_manager = callback_manager.on_chain_start(
298 dumpd(self),
299 inputs,
300 name=run_name,
301 )
302 try:
303 outputs = (
--> 304 self._call(inputs, run_manager=run_manager)
305 if new_arg_supported
306 else self._call(inputs)
307 )
308 except BaseException as e:
309 run_manager.on_chain_error(e)
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:1146, in AgentExecutor._call(self, inputs, run_manager)
1144 # We now enter the agent loop (until it returns something).
1145 while self._should_continue(iterations, time_elapsed):
-> 1146 next_step_output = self._take_next_step(
1147 name_to_tool_map,
1148 color_mapping,
1149 inputs,
1150 intermediate_steps,
1151 run_manager=run_manager,
1152 )
1153 if isinstance(next_step_output, AgentFinish):
1154 return self._return(
1155 next_step_output, intermediate_steps, run_manager=run_manager
1156 )
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:996, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
994 tool_run_kwargs["llm_prefix"] = ""
995 # We then call the tool on the tool input to get an observation
--> 996 observation = tool.run(
997 agent_action.tool_input,
998 verbose=self.verbose,
999 color=color,
1000 callbacks=run_manager.get_child() if run_manager else None,
1001 **tool_run_kwargs,
1002 )
1003 else:
1004 tool_run_kwargs = self.agent.tool_run_logging_kwargs()
File /opt/conda/lib/python3.10/site-packages/langchain/tools/base.py:365, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)
363 except (Exception, KeyboardInterrupt) as e:
364 run_manager.on_tool_error(e)
--> 365 raise e
366 else:
367 run_manager.on_tool_end(
368 str(observation), color=color, name=self.name, **kwargs
369 )
File /opt/conda/lib/python3.10/site-packages/langchain/tools/base.py:337, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)
334 try:
335 tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
336 observation = (
--> 337 self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
338 if new_arg_supported
339 else self._run(*tool_args, **tool_kwargs)
340 )
341 except ToolException as e:
342 if not self.handle_tool_error:
File /opt/conda/lib/python3.10/site-packages/langchain/tools/base.py:510, in Tool._run(self, run_manager, *args, **kwargs)
507 if self.func:
508 new_argument_supported = signature(self.func).parameters.get("callbacks")
509 return (
--> 510 self.func(
511 *args,
512 callbacks=run_manager.get_child() if run_manager else None,
513 **kwargs,
514 )
515 if new_argument_supported
516 else self.func(*args, **kwargs)
517 )
518 raise NotImplementedError("Tool does not support sync")
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:505, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
503 if len(args) != 1:
504 raise ValueError("`run` supports only one positional argument.")
--> 505 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
506 _output_key
507 ]
509 if kwargs and not args:
510 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
511 _output_key
512 ]
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
313 inputs, outputs, return_only_outputs
314 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:304, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
297 run_manager = callback_manager.on_chain_start(
298 dumpd(self),
299 inputs,
300 name=run_name,
301 )
302 try:
303 outputs = (
--> 304 self._call(inputs, run_manager=run_manager)
305 if new_arg_supported
306 else self._call(inputs)
307 )
308 except BaseException as e:
309 run_manager.on_chain_error(e)
File /opt/conda/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py:139, in BaseRetrievalQA._call(self, inputs, run_manager)
137 else:
138 docs = self._get_docs(question) # type: ignore[call-arg]
--> 139 answer = self.combine_documents_chain.run(
140 input_documents=docs, question=question, callbacks=_run_manager.get_child()
141 )
143 if self.return_source_documents:
144 return {self.output_key: answer, "source_documents": docs}
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:510, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
505 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
506 _output_key
507 ]
509 if kwargs and not args:
--> 510 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
511 _output_key
512 ]
514 if not kwargs and not args:
515 raise ValueError(
516 "`run` supported with either positional arguments or keyword arguments,"
517 " but none were provided."
518 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:286, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
250 def __call__(
251 self,
252 inputs: Union[Dict[str, Any], Any],
(...)
259 include_run_info: bool = False,
260 ) -> Dict[str, Any]:
261 """Execute the chain.
262
263 Args:
(...)
284 `Chain.output_keys`.
285 """
--> 286 inputs = self.prep_inputs(inputs)
287 callback_manager = CallbackManager.configure(
288 callbacks,
289 self.callbacks,
(...)
294 self.metadata,
295 )
296 new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:443, in Chain.prep_inputs(self, inputs)
441 external_context = self.memory.load_memory_variables(inputs)
442 inputs = dict(inputs, **external_context)
--> 443 self._validate_inputs(inputs)
444 return inputs
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:195, in Chain._validate_inputs(self, inputs)
193 missing_keys = set(self.input_keys).difference(inputs)
194 if missing_keys:
--> 195 raise ValueError(f"Missing some input keys: {missing_keys}")
ValueError: Missing some input keys: {'input'}
```
I also tried calling the agent like this:
```
chat_history = []
question = "What are the closing hours?"
agent({"input": question, "chat_history": chat_history})
```
and got the same error.
I tried to use an agent executor, moving the memory from the agent to the executor but I get the following error:
```
agent_executor = AgentExecutor(agent=agent,
tools=tools,
memory=memory,
verbose=True)
agent_executor.invoke({"input": "What are the closing hours?"})['output']
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[61], line 1
----> 1 agent_executor.invoke({"input": "What are the closing hours?"})['output']
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:87, in Chain.invoke(self, input, config, **kwargs)
80 def invoke(
81 self,
82 input: Dict[str, Any],
83 config: Optional[RunnableConfig] = None,
84 **kwargs: Any,
85 ) -> Dict[str, Any]:
86 config = config or {}
---> 87 return self(
88 input,
89 callbacks=config.get("callbacks"),
90 tags=config.get("tags"),
91 metadata=config.get("metadata"),
92 run_name=config.get("run_name"),
93 **kwargs,
94 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
313 inputs, outputs, return_only_outputs
314 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:304, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
297 run_manager = callback_manager.on_chain_start(
298 dumpd(self),
299 inputs,
300 name=run_name,
301 )
302 try:
303 outputs = (
--> 304 self._call(inputs, run_manager=run_manager)
305 if new_arg_supported
306 else self._call(inputs)
307 )
308 except BaseException as e:
309 run_manager.on_chain_error(e)
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:1146, in AgentExecutor._call(self, inputs, run_manager)
1144 # We now enter the agent loop (until it returns something).
1145 while self._should_continue(iterations, time_elapsed):
-> 1146 next_step_output = self._take_next_step(
1147 name_to_tool_map,
1148 color_mapping,
1149 inputs,
1150 intermediate_steps,
1151 run_manager=run_manager,
1152 )
1153 if isinstance(next_step_output, AgentFinish):
1154 return self._return(
1155 next_step_output, intermediate_steps, run_manager=run_manager
1156 )
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:933, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
930 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
932 # Call the LLM to see what to do.
--> 933 output = self.agent.plan(
934 intermediate_steps,
935 callbacks=run_manager.get_child() if run_manager else None,
936 **inputs,
937 )
938 except OutputParserException as e:
939 if isinstance(self.handle_parsing_errors, bool):
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:375, in RunnableAgent.plan(self, intermediate_steps, callbacks, **kwargs)
363 """Given input, decided what to do.
364
365 Args:
(...)
372 Action specifying what tool to use.
373 """
374 inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
--> 375 output = self.runnable.invoke(inputs, config={"callbacks": callbacks})
376 return output
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:87, in Chain.invoke(self, input, config, **kwargs)
80 def invoke(
81 self,
82 input: Dict[str, Any],
83 config: Optional[RunnableConfig] = None,
84 **kwargs: Any,
85 ) -> Dict[str, Any]:
86 config = config or {}
---> 87 return self(
88 input,
89 callbacks=config.get("callbacks"),
90 tags=config.get("tags"),
91 metadata=config.get("metadata"),
92 run_name=config.get("run_name"),
93 **kwargs,
94 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
313 inputs, outputs, return_only_outputs
314 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:304, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
297 run_manager = callback_manager.on_chain_start(
298 dumpd(self),
299 inputs,
300 name=run_name,
301 )
302 try:
303 outputs = (
--> 304 self._call(inputs, run_manager=run_manager)
305 if new_arg_supported
306 else self._call(inputs)
307 )
308 except BaseException as e:
309 run_manager.on_chain_error(e)
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:1146, in AgentExecutor._call(self, inputs, run_manager)
1144 # We now enter the agent loop (until it returns something).
1145 while self._should_continue(iterations, time_elapsed):
-> 1146 next_step_output = self._take_next_step(
1147 name_to_tool_map,
1148 color_mapping,
1149 inputs,
1150 intermediate_steps,
1151 run_manager=run_manager,
1152 )
1153 if isinstance(next_step_output, AgentFinish):
1154 return self._return(
1155 next_step_output, intermediate_steps, run_manager=run_manager
1156 )
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:933, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
930 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
932 # Call the LLM to see what to do.
--> 933 output = self.agent.plan(
934 intermediate_steps,
935 callbacks=run_manager.get_child() if run_manager else None,
936 **inputs,
937 )
938 except OutputParserException as e:
939 if isinstance(self.handle_parsing_errors, bool):
TypeError: Agent.plan() got multiple values for argument 'intermediate_steps'
```
Im not sure what im doing wrong.
@dosu-bot
### Suggestion:
- Maybe using a template for the agent can work? I dont know if its possible, but im also having trouble forcing it to use only the tools to search the info or else answer that it doesnt know.
- Using LCEL to add a parser but im not familiar with LCEL.
- Change the aproach creating a custom tool/agent/chain.
|
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| 1,967,136,984 |
PR_kwDOIPDwls5eDhQR
| 12,520 |
Add unit tests for document_transformers/beautiful_soup_transformer.py
|
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[] | null | 1 | 2023-10-29T20:08:53 | 2023-10-29T23:24:47 | 2023-10-29T23:24:47 |
CONTRIBUTOR
| null |
- **Description:**
* Add unit tests for document_transformers/beautiful_soup_transformer.py
* Basic functionality is tested (extract tags, remove tags, drop lines)
* add a FIXME comment about the order of tags that is not preserved
(and a passing test, but with the expected tags now out-of-order)
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @rlancemartin
- **Twitter handle:** `peter_v`
Please make sure your PR is passing linting and testing before submitting.
=> OK: I ran `make format`, `make test` (passing after install of beautifulsoup4) and `make lint`.
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PR_kwDOIPDwls5eDaEq
| 12,518 |
bind_functions convenience method
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[] | null | 1 | 2023-10-29T18:12:52 | 2023-10-31T14:15:39 | 2023-10-31T14:15:37 |
COLLABORATOR
| null |
I always take 20-30 seconds to re-discover where the `convert_to_openai_function` wrapper lives in our codebase. Chat langchain [has no clue](https://smith.langchain.com/public/3989d687-18c7-4108-958e-96e88803da86/r) what to do either. There's the older `create_openai_fn_chain` , but we haven't been recommending it in LCEL. The example we show in the [cookbook](https://python.langchain.com/docs/expression_language/how_to/binding#attaching-openai-functions) is really verbose.
General function calling should be as simple as possible to do, so this seems a bit more ergonomic to me (feel free to disagree). Another option would be to directly coerce directly in the class's init (or when calling invoke), if provided. I'm not 100% set against that. That approach may be too easy but not simple. This PR feels like a decent compromise between simple and easy.
```
from enum import Enum
from typing import Optional
from pydantic import BaseModel, Field
class Category(str, Enum):
"""The category of the issue."""
bug = "bug"
nit = "nit"
improvement = "improvement"
other = "other"
class IssueClassification(BaseModel):
"""Classify an issue."""
category: Category
other_description: Optional[str] = Field(
description="If classified as 'other', the suggested other category"
)
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI().bind_functions([IssueClassification])
llm.invoke("This PR adds a convenience wrapper to the bind argument")
# AIMessage(content='', additional_kwargs={'function_call': {'name': 'IssueClassification', 'arguments': '{\n "category": "improvement"\n}'}})
```
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| 1,967,066,381 |
I_kwDOIPDwls51Pw0N
| 12,517 |
Getting invalid output error when using score evaluator
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[] | null | 11 | 2023-10-29T16:51:32 | 2023-10-30T19:31:02 | null |
NONE
| null |
### System Info
platform = Windows 11. Linux Ubuntu 22.04
python = 3.10
langchain = 0.0.325
### Who can help?
@hwchase17 @agola11 @eyu
When trying to reproduce the example of the Scoring evaluator [https://python.langchain.com/docs/guides/evaluation/string/scoring_eval_chain#usage-without-references] with the next code
`evaluator = load_evaluator("labeled_score_string", criteria=criteria, llm=llm, normalize_by=10)
eval_result = evaluator.evaluate_strings(prediction="You can find them in the dresser's third drawer.", reference="The socks are in the third drawer in the dresser", input="Where are my socks?")`
I'm getting the error `ValueError: Invalid output: . Output must contain a double bracketed string with the verdict between 1 and 10.`
Thanks!!
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`
from langchain.evaluation import load_evaluator`
`evaluator = load_evaluator("labeled_score_string", criteria=criteria, llm=llm, normalize_by=10)
eval_result = evaluator.evaluate_strings(prediction="You can find them in the dresser's third drawer.", reference="The socks are in the third drawer in the dresser", input="Where are my socks?")
`
### Expected behavior
` {'reasoning': "The assistant's response is helpful, accurate, and directly answers the user's question. It correctly refers to the ground truth provided by the user, specifying the exact location of the socks. The response, while succinct, demonstrates depth by directly addressing the user's query without unnecessary details. Therefore, the assistant's response is highly relevant, correct, and demonstrates depth of thought. \n\nRating: [[10]]", 'score': 10}`
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| 1,966,988,795 |
PR_kwDOIPDwls5eDD5-
| 12,516 |
Fixed broken link in Quickstart page
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[] | null | 1 | 2023-10-29T13:21:16 | 2023-11-02T20:00:54 | 2023-11-02T20:00:53 |
CONTRIBUTOR
| null |
- **Description:**
Corrected a specific link within the documentation.
- **Issue:**
#12490
- **Dependencies:**
- **Tag maintainer:**
- **Twitter handle:**
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| 1,966,981,612 |
I_kwDOIPDwls51PcHs
| 12,515 |
Retrieval extraction of Tabular data
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[] | null | 4 | 2023-10-29T13:00:45 | 2023-10-29T13:40:49 | null |
NONE
| null |
### Issue you'd like to raise.
hello everyone
I have a website which needs to pe scrapped and the
website has the tabular data
so when I scrap the data using web loaders in langchain and build a llm on top of it
it is not able to answer the query.
So what can be the suitable approach to solve this issue
### Suggestion:
_No response_
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I_kwDOIPDwls51PXy9
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chatglm3-6b-32k----Please support chatglm3-6b-32k thank you very much
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[] | null | 2 | 2023-10-29T12:09:10 | 2023-12-27T09:15:33 | null |
NONE
| null |
### Issue with current documentation:
Please support chatglm3-6b-32k thank you very much
### Idea or request for content:
_No response_
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| 1,966,941,802 |
I_kwDOIPDwls51PSZq
| 12,513 |
Celery getting hung when langchain gets ratelimit error
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[] | null | 1 | 2023-10-29T11:01:23 | 2023-10-29T11:12:42 | null |
NONE
| null |
### System Info
MacOS, 8GB RAM, M2
### Who can help?
@hwchase17 @agola11
Langchain Chat Models, Celery (apply.async())
### 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
Create a function that makes some calls to the OpenAI API.
Run that using Celery's apply.async().
When ratelimit error is hit, Langchain will retry after 4 seconds but the Celery will get hung indefinitely.
### Expected behavior
Langchain should be able to retry after 4 seconds without affecting Celery job.
|
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I_kwDOIPDwls51PMJY
| 12,512 |
Use also bigger files as input to get answers?
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[] | null | 1 | 2023-10-29T09:40:27 | 2023-10-29T09:49:08 | null |
NONE
| null |
### Issue you'd like to raise.
Hello - i try vectorize a local txt-file and get an answer out of it using the following code:
```
from langchain.document_loaders import DirectoryLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
import os
### Interpret content of local files
text_loader_kwargs={'autodetect_encoding': True}
loader = TextLoader("url.txt", encoding="utf8")
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=500,
chunk_overlap=50)
texts = splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device': 'cpu'})
# create and save the local database
db = FAISS.from_documents(texts, embeddings)
db.save_local("robalf")
### prepare AI with local files
# prepare the template we will use when prompting the AI
template = """Use the following text to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Context: {context}
Question: {question}
Only return the helpful answer below and nothing else.
Helpful answer:
"""
# load the language model
fn = os.path.join("G:\DEV\TEMPDEV", "llama-2-7b-chat.ggmlv3.q8_0.bin")
llm = CTransformers(model=fn,
model_type='llama',
config={'max_new_tokens': 1024, 'temperature': 0.01})
# load the interpreted information from the local database
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device': 'cpu'})
db = FAISS.load_local("robalf", embeddings)
# prepare a version of the llm pre-loaded with the local content
retriever = db.as_retriever(search_kwargs={'k': 2})
prompt = PromptTemplate(
template=template,
input_variables=['context', 'question'])
qa_llm = RetrievalQA.from_chain_type(llm=llm,
chain_type='stuff',
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={'prompt': prompt})
# ask the AI chat about information in our local files
prompt = "Write a maximum of 20 products produced or sold by the company in the text below. "
output = qa_llm({'query': prompt})
print(output["result"])
```
When i run this with a small url.txt everything works fine and i get an answer to the text.
But when i use a bigger url.txt i get the following error:
```
Number of tokens (513) exceeded maximum context length (512).
Number of tokens (514) exceeded maximum context length (512).
Number of tokens (515) exceeded maximum context length (512).
Number of tokens (516) exceeded maximum context length (512).
Number of tokens (517) exceeded maximum context length (512).
Number of tokens (518) exceeded maximum context length (512).
Number of tokens (519) exceeded maximum context length (512).
Number of tokens (520) exceeded maximum context length (512).
Number of tokens (521) exceeded maximum context length (512).
Number of tokens (522) exceeded maximum context length (512).
```
How can i use also bigger files as input and get answers?
### Suggestion:
How can i use also bigger files as input and get answers?
|
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I_kwDOIPDwls51PJH2
| 12,511 |
DOC: how to output a prompt for each call, in chain_type=refine
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[] | null | 1 | 2023-10-29T08:59:04 | 2023-10-29T09:07:04 | null |
NONE
| null |
### Issue with current documentation:
In scenarios where chain_type=refine, how do I output a prompt for each call?
```
chain = load_qa_chain(llm=llm, chain_type='refine', verbose=True)
```
The test found that setting verbose=True, also only outputs the prompt for the last llm request.
### Idea or request for content:
_No response_
|
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| 1,966,902,078 |
I_kwDOIPDwls51PIs-
| 12,510 |
DOC: How do I control the time interval for invoking LLM
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[] | null | 1 | 2023-10-29T08:53:04 | 2023-10-29T09:03:56 | null |
NONE
| null |
### Issue with current documentation:
For example, in scenarios where chain_type=refine, LLM calls are made multiple times.
How do you control the intervals between LLM calls?
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls51PGtM
| 12,509 |
DOC: How to end the refine process early in langchain？
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[] | null | 1 | 2023-10-29T08:25:39 | 2023-10-29T08:33:03 | null |
NONE
| null |
### Issue with current documentation:
The refine mode of langchain has already found the answer, and then requesting the interface is a waste of resources, can it be ended early?
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5eCtgg
| 12,508 |
Patch forward ref bug
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[] | null | 1 | 2023-10-29T07:07:04 | 2023-10-29T07:53:02 | 2023-10-29T07:53:02 |
COLLABORATOR
| null |
Currently this gives a bug:
```
from langchain.schema.runnable import RunnableLambda
bound = RunnableLambda(lambda x: x).with_config({"callbacks": []})
# ConfigError: field "callbacks" not yet prepared so type is still a ForwardRef, you might need to call RunnableConfig.update_forward_refs().
```
Rather than deal with cyclic imports and extra load time, etc., I think it makes sense to just have a separate Callbacks definition here that is a relaxed typehint.
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I_kwDOIPDwls51O_VY
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Issue: ImportError: cannot import name 'VectorStore' from 'langchain.vectorstores'
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[] | null | 6 | 2023-10-29T06:31:12 | 2023-11-03T22:08:49 | null |
NONE
| null |
### Issue you'd like to raise.
Since 0.0.324, "from langchain.vectorstores import VectorStore" doesn't work anymore, any idea how to fix this?
Thanks!
### Suggestion:
_No response_
|
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I_kwDOIPDwls51O-tu
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Issue: agent tool has been set with return_direct, when using agent.acall, just return Thought to user
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[] | null | 1 | 2023-10-29T06:18:45 | 2023-10-29T06:20:52 | null |
NONE
| null |
### Issue you'd like to raise.
when using agent.run, tool's observation is returned.
when using agent.acall, just returned agent's Thought to user
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5eCqhR
| 12,505 |
Evaluation Callback Multi Response
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[] | null | 1 | 2023-10-29T05:57:41 | 2023-10-29T06:18:30 | 2023-10-29T06:18:29 |
COLLABORATOR
| null |
1. Allow run evaluators to return {"results": [list of evaluation results]} in the evaluator callback.
2. Allows run evaluators to pick the target run ID to provide feedback to
(1) means you could do something like a function call that populates a full rubric in one go (not sure how reliable that is in general though) rather than splitting off into separate LLM calls - cheaper and less code to write
(2) means you can provide feedback to runs on subsequent calls. Immediate use case is if you wanted to add an evaluator to a chat bot and assign to assign to previous conversation turns
have a corresponding one in the SDK
|
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| 1,966,856,123 |
PR_kwDOIPDwls5eCqaW
| 12,504 |
Replace You with Tavily in xml agent
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[] | null | 3 | 2023-10-29T05:54:49 | 2023-12-01T23:08:04 | 2023-10-30T19:51:01 |
COLLABORATOR
| null |
You requires an email to get an API key which IMO is too much friction. Tavily is quick
|
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| 1,966,819,941 |
I_kwDOIPDwls51O0pl
| 12,503 |
Issue: StreamingStdOutCallbackHandler and FinalStreamingStdOutCallbackHandler
|
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[] | null | 3 | 2023-10-29T02:45:00 | 2023-10-29T03:16:06 | null |
NONE
| null |
### Issue you'd like to raise.
Hi,
I build a chatbot using conversationchain. I want to final output to be steamed. I tried StreamingStdOutCallbackHandler and FinalStreamingStdOutCallbackHandler.
The problem with StreamingStdOutCallbackHandler is that it also prints out the memory of the chain.
The problem with FinalStreamingStdOutCallbackHandler is that it did print the final output, but the output is just printed out at once rather than streamed.
How can I fix it?
Thanks,
### Suggestion:
_No response_
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PR_kwDOIPDwls5eChYH
| 12,502 |
Sphinxbio nls/add plate chain template
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[] | null | 1 | 2023-10-29T01:50:58 | 2023-10-29T04:48:18 | 2023-10-29T04:48:18 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5eCcH5
| 12,501 |
Add platechain template
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<!-- 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/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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various templates improvements
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PR_kwDOIPDwls5eCazM
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add a template for the package readme
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PR_kwDOIPDwls5eCXeU
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PR_kwDOIPDwls5eCVmS
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docs(prompt_templates): fix typo in prompt template
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[] | null | 1 | 2023-10-28T21:52:24 | 2023-10-29T21:52:38 | 2023-10-29T21:52:38 |
CONTRIBUTOR
| null |
- **Description:** Fixes a small typo in the [Prompt template document](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/)
- **Dependencies:** none
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PR_kwDOIPDwls5eCU8S
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Masking of API Key for GooseAI LLM
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CONTRIBUTOR
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Description: Add masking of API Key for GooseAI LLM when printed.
Issue: https://github.com/langchain-ai/langchain/issues/12165
Dependencies: None
Tag maintainer: @eyurtsev
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I_kwDOIPDwls51Oif3
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SQLDatabaseChain sharing data that's not in database
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[] | null | 9 | 2023-10-28T21:30:30 | 2023-12-05T15:48:07 | null |
NONE
| null |
### Issue you'd like to raise.
Okay I have tried both SQL database chain and create SQL agent from agents and what i have seen is that after run and it returns zero records from the database and LLM gives the output from its own. So basically I have only one fruit in my database and if I ask what fruit contains vitamin c which is not in the database and it fetch me the result from its own knowledge base. I tried adding the instructions in the prompt like if query return zero records or observation is empty or the result of the SQL. Query is empty, Do not share anything on your own And just convey the message to the user that for your certain filter criteria that is no records Try different query or elaborate your request but it doesn't seem to be working. is there anyting we can do to fix it?
### Suggestion:
_No response_
|
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reopened
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| 1,966,738,613 |
I_kwDOIPDwls51Ogy1
| 12,494 |
email with metadata from pdfs
|
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[] | null | 1 | 2023-10-28T21:01:17 | 2023-10-28T21:03:41 | null |
NONE
| null |
### Feature request
parse the text in a pdf to determine whether it contains header fields such as From:, To:, Date: etc which make it likely that the original data was an email. if so, return the contents of those fields as Document metadata which can, for example, be used as metadata in a database. (reopening of https://github.com/langchain-ai/langchain/issues/8094)
### Motivation
Sometimes it as important to know who said what to whom when as it is to determine what actual facts are. Investigative reporting is one example. There are adequate tools to retrieve email and its metadata from Gmail or Exchange. A similar tool for email which has been saved as a collection of PDFs which parses and retrieves the metadata will make these document collections more accessible and useful.
### Your contribution
I have created a pdf to email tool in the proper format to incorporated with existing langchain document loaders. It's at https://github.com/tevslin/emailai, There's been enough interest so that I'm forking, linting, and will submit a pr.
|
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| 1,966,732,154 |
PR_kwDOIPDwls5eCSGO
| 12,493 |
change project template
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[] | null | 2 | 2023-10-28T20:33:01 | 2023-10-30T21:06:31 | 2023-10-30T21:06:30 |
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| 1,966,724,161 |
PR_kwDOIPDwls5eCQkm
| 12,492 |
add gha for cli
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[] | null | 1 | 2023-10-28T20:03:10 | 2023-10-29T04:49:29 | 2023-10-29T04:49:28 |
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| 1,966,722,125 |
PR_kwDOIPDwls5eCQNd
| 12,491 |
Harrison/quick start
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| 1,966,711,948 |
I_kwDOIPDwls51OaSM
| 12,490 |
DOC: Broken link in Quickstart page
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[] | null | 0 | 2023-10-28T19:20:13 | 2023-10-28T19:22:19 | null |
NONE
| null |
### Issue with current documentation:
# Issue
On the documentation page: https://python.langchain.com/docs/get_started/quickstart
In the Next Steps section:
- Explore [end-to-end use cases](https://python.langchain.com/docs/use_cases) results in page not found
## Screenshot
<img width="960" alt="Langchain_docs" src="https://github.com/langchain-ai/langchain/assets/63769209/b1618fad-b7d2-4784-a30f-f6361f81b20a">
<img width="960" alt="bug" src="https://github.com/langchain-ai/langchain/assets/63769209/b637d0fa-aeb4-439b-b598-e1855c35fdaa">
### Idea or request for content:
I think that the above issue can be resolved by replacing the above link with this: https://python.langchain.com/docs/use_cases/qa_structured/sql
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PR_kwDOIPDwls5eCMJB
| 12,489 |
Adds embeddings filter option to return scores in state
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[] | null | 4 | 2023-10-28T18:43:12 | 2023-11-09T01:50:07 | 2023-11-09T01:50:06 |
CONTRIBUTOR
| null |
CC @baskaryan @assafelovic
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| 1,966,694,473 |
I_kwDOIPDwls51OWBJ
| 12,488 |
Getting error while using Redistore as docstore in parent document retreival
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[] | null | 2 | 2023-10-28T18:21:41 | 2023-11-06T22:21:28 | null |
NONE
| null |
### System Info
i am getting an error while using Redistore as docstore `A tuple item must be str, int, float or bytes.`. I do not get error when using the InMemorystore. the problem is with mset function, in redistore mset throws error when i pass a document object, but redistore works if just pass a string.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
i am getting an error while using Redistore as docstore `A tuple item must be str, int, float or bytes.`. I do not get error when using the InMemorystore. the problem is with mset function, in redistore mset throws error when i pass a document object, but redistore works if just pass a string.
### Expected behavior
i am getting an error while using Redistore as docstore `A tuple item must be str, int, float or bytes.`. I do not get error when using the InMemorystore. the problem is with mset function, in redistore mset throws error when i pass a document object, but redistore works if just pass a string.
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PR_kwDOIPDwls5eCBNK
| 12,487 |
bump to 36
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PR_kwDOIPDwls5eB-f3
| 12,486 |
add cookbook for selectins llms based on context length
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PR_kwDOIPDwls5eB7ok
| 12,484 |
Fix poetry dependency resolution in devcontainer and simplify setup
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CONTRIBUTOR
| null |
**Description:** Fix poetry dependency resolution within devcontainer and simplify devcontainer configuration along the way:
- Specify devcontainer config in devcontainer.json only (get rid of dev Dockerfile)
- devcontainer uses same poetry version as container running unit tests
- Install poetry via devcontainer feature instead of using multi-stage build
- Move setup tasks (configuring shell completions, installing dependencies) to dedicated file (setup.sh)
**Issue:**
- #7825
- #8277
**Dependencies:** None
**Tag maintainer:** @eyurtsev
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| 1,966,594,109 |
I_kwDOIPDwls51N9g9
| 12,483 |
langchain Ctransformers caching and usage in Dockerfile volume
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[] | null | 2 | 2023-10-28T13:22:30 | 2023-10-28T13:47:20 | null |
NONE
| null |
### Issue you'd like to raise.
How can I cache llama models that called in langchain llms CTransformers and add it to a docker image to prevent it download the model every time I called in a cloud environment?
As I researched, I saw no clear explanation of how to cache models in Ctransformers.
### Suggestion:
_No response_
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PR_kwDOIPDwls5eBygK
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更新了February的拼写错误
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NONE
| null |
There was a typo in the “Langhian/docs/docs/integrations/retrievers/tavily.ipynb”, "February" was misspelled.
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PR_kwDOIPDwls5eByd3
| 12,481 |
Docs: Update Code Writing example for import location change of PythonREPL
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[] | null | 3 | 2023-10-28T11:59:01 | 2023-10-28T15:58:55 | 2023-10-28T15:58:54 |
NONE
| null |
**Description**: Fixes documentation example for Code Writing after refactoring of imports for PythonREPL in experimental lib
**Issue**: Fixes #12480
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I_kwDOIPDwls51N24H
| 12,480 |
DOC: Code Writing example throws error with PythonREPL import
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[] | null | 2 | 2023-10-28T11:56:06 | 2023-10-28T15:59:15 | 2023-10-28T15:59:14 |
NONE
| null |
### Issue with current documentation:
I am attempting to run: https://python.langchain.com/docs/expression_language/cookbook/code_writing
and I'm seeing this error:
```
from langchain_experimental.utilities import PythonREPL
ImportError: cannot import name 'PythonREPL' from 'langchain_experimental.utilities'
```
### Idea or request for content:
I assume the fix is below
```
$ git diff
diff --git a/docs/docs/expression_language/cookbook/code_writing.ipynb b/docs/docs/expression_language/cookbook/code_writing.ipynb
index 21ab53601..bf1840c5a 100644
--- a/docs/docs/expression_language/cookbook/code_writing.ipynb
+++ b/docs/docs/expression_language/cookbook/code_writing.ipynb
@@ -20,7 +20,7 @@
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate\n",
"from langchain.schema.output_parser import StrOutputParser\n",
- "from langchain_experimental.utilities import PythonREPL"
+ "from langchain_experimental.utilities.python import PythonREPL"
]
},
{
```
Will submit a PR with above
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I_kwDOIPDwls51N2Di
| 12,479 |
Get document ids from ConversationalRetrievalChain's reponse
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[] | null | 2 | 2023-10-28T11:44:24 | 2023-10-29T16:42:00 | 2023-10-29T16:41:59 |
NONE
| null |
### Feature request
Module: langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain
`qa = ConversationalRetrievalChain(
retriever=self.vector_store.as_retriever(), # type: ignore
combine_docs_chain=self.doc_chain,
question_generator=LLMChain(
llm=self.answering_llm, prompt=CONDENSE_QUESTION_PROMPT
),
verbose=False,
)`
`model_response = qa(
{
"question": user_message,
"chat_history": formatted_chat_history,
"custom_personality": self.prompt_content,
}
)`
I would like that the model_response (the result from called the ConversationalRetrievalChain) returns the document ids in which the answer is based
### Motivation
My motivation is to be able to document which documents is the model basing its responses to further analyse and use this input for user experience improvement and transparency.
One possibility would be to use the _get_docs method under ConversationalRetrievalChain, but this would imply calling twice the model
### Your contribution
As of now the model_response returns:
`{'question': 'Who won the Olympics in 92?', 'chat_history': [()], 'custom_personality': 'xxxxxx
', 'answer': 'The US won the Olympics'}`
My proposal would be:
`{'question': 'Who won the Olympics in 92?', 'chat_history': [()], 'custom_personality': 'xxxxxx
', 'answer': 'The US won the Olympics', 'documents_source': [Document(), Document()...]}`
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PR_kwDOIPDwls5eBte1
| 12,478 |
sagemaker streaming fix for _call and _acall
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[] | null | 3 | 2023-10-28T10:46:42 | 2023-10-30T23:16:44 | null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** sagemaker streaming fix for _call and _acall,
- **Issue:** the issue # it fixes sagemaker streaming fix for _call and _acall,
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,966,539,154 |
PR_kwDOIPDwls5eBstp
| 12,477 |
Bedrock async methods
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[] | null | 18 | 2023-10-28T10:32:59 | 2024-01-09T23:44:09 | null |
NONE
| null |
Description: Added support for asynchronous streaming in the Bedrock class and corresponding tests.
Primarily:
async def aprepare_output_stream
async def _aprepare_input_and_invoke_stream
async def _astream
async def _acall
I've ensured that the code adheres to the project's linting and formatting standards by running make format, make lint, and make test.
Issue: #12054, #11589
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle: @dominic_lovric
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SecretStr for fireworks api
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[] | null | 1 | 2023-10-28T06:16:28 | 2023-10-31T01:17:53 | 2023-10-31T01:17:53 |
CONTRIBUTOR
| null |
- **Description:** This pull request removes secrets present in raw format,
- **Issue:** Fireworks api key was exposed when printing out the langchain object [#12165](https://github.com/langchain-ai/langchain/issues/12165)
- **Maintainer:** @eyurtsev
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I_kwDOIPDwls51NW42
| 12,474 |
Support TensorRT-LLM?
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[] | null | 3 | 2023-10-28T05:04:42 | 2023-12-22T02:32:53 | null |
NONE
| null |
### Feature request
Any plan to support nvidia's latest TensorRT-LLM, maybe via triton-inference-server backend?
### Motivation
New integration
### Your contribution
Test
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