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utils/transcripts.py for chunking preserving previous behaviour as default for #8087
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NONE
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fixes #8087
added chunking mechanism for for transcript loaders srt.py and youtubeloader.py so user could optionally load transcript for a certain duration
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fix:get bilibili subtitles
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CONTRIBUTOR
| null |
- Description: fix the Loader 'BiliBiliLoader'
- Issue: the API response was changed
The previously used API no longer returns the "subtitle_url" property.
We should use another API to get `subtitle_url` property.
The `subtitle_url` returned by this API does not include the http schema and needs to be added.
- Dependencies: Nope
- Tag maintainer: @rlancemartin
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Add support for load_chat_model
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CONTRIBUTOR
| null |
- Description:
- Add `load_chat_model()` and `load_chat_model_from_config()` in `langchain.chat_model.loading` module
- Update `_load_llm_chain()` to use the above functions in case of LLMs that are Chat Models
- Fix warning while loading `AzureChatOpenAI` and `ChatOpenAI`: WARNING! engine is not default parameter.
- **TODO**: I'll add/update unit tests once I get a go-ahead from maintainers for these new additions.
- Issue:
- https://github.com/langchain-ai/langchain/issues/2627
- Related PR that was closed: https://github.com/langchain-ai/langchain/pull/1715
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: None
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Adding Adobe PDF extraction API as an additional langchain document loader
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[] | null | 3 | 2023-07-24T05:21:58 | 2023-10-24T11:49:27 | null |
NONE
| null |
### Feature request
Hi langchain team!
I'd like to contribute this feature to the langchain document loaders.
There are multiple pros for using Adobe API instead of the existing libraries for converting pdf to text and other metadata; e.g, adobe API allows for extraction of tables and figures in pdf documents as separate .csv and .png files, respectively.
These are of course in addition to raw text extraction which in my opinion works better most of the time compared to other libraries.
Here are a few links to examples and source code:
[Adobe python API example](https://developer.adobe.com/document-services/docs/overview/pdf-extract-api/quickstarts/python/)
[Adobe SDK](https://github.com/adobe/pdfservices-python-sdk-samples/tree/main/src/extractpdf)
### Motivation
I needed to extract the tables in pdf files in a standard format to extract the data in them more carefully. I was also not satisfied by the existing libraries performance on raw text extraction.
### Your contribution
I'd like to contribute this feature as a new document loader in langchain. Let me know if I can start working on it.
Thanks!
|
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Harrison/hugginggpt
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[] | null | 1 | 2023-07-24T04:59:48 | 2023-07-24T14:36:26 | 2023-07-24T14:36:24 |
COLLABORATOR
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| 1,817,621,937 |
I_kwDOIPDwls5sVrWx
| 8,161 |
langchain.llms.Petals doesnt support llama models
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[] | null | 2 | 2023-07-24T04:56:39 | 2023-11-04T16:05:05 | 2023-11-04T16:05:05 |
NONE
| null |
### Feature request
support for llama models for petals module
### Motivation
i'm furstated when i want to use petals (distributed model) for llama2, my gpu is not enough to run all models
### Your contribution
-
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PR_kwDOIPDwls5WMIRb
| 8,160 |
Add support for load_chat_model
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,817,560,003 |
I_kwDOIPDwls5sVcPD
| 8,159 |
Migration OpenAIEmbedding from Azure to OpenAI reproduce gives an error message "ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions."
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[] | null | 2 | 2023-07-24T03:58:46 | 2023-11-03T16:06:27 | 2023-11-03T16:06:27 |
NONE
| null |
### System Info
python = ">=3.9,<3.12"
fire = "0.5.0"
pandas = "2.0.2"
sqlalchemy = "2.0.15"
rich = "13.4.1"
mysqlclient = "2.1.1"
pandera = "0.15.1"
openai = "0.27.7"
guidance = "0.0.62"
langchain = "0.0.221"
textual = "^0.29.0"
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Prepare data. In our case, there are 3576 documents.
2. Migrate API
before migration:
```python
embeddings = OpenAIEmbeddings(
model="text-embedding-ada-002",
deployment=<MY_DEPLOYMENT_NAME>,
chunk_size=1,
openai_api_base=os.environ["OPENAI_API_BASE"],
openai_api_key=os.environ["OPENAI_API_KEY"],
openai_api_type="azure",
)
```
after migration:
```python
embeddings = OpenAIEmbeddings(
model="text-embedding-ada-002",
openai_api_key=os.environ["OPENAI_API_KEY"],
openai_api_type="openai",
)
```
3. Make FAISS DB
The code are the same as that of before-migration version.
db = FAISS.from_documents(docs, embeddings)
4. Error occurs
```
Traceback (most recent call last):
File "/path/make_dataframe.py", line 54, in <module>
db = FAISS.from_documents(docs, embeddings)
File "/path/.venv/lib/python3.9/site-packages/langchain/vectorstores/base.py", line 337, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File "/path/.venv/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 554, in from_texts
return cls.__from(
File "/path/.venv/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 506, in __from
vector = np.array(embeddings, dtype=np.float32)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (3576,) + inhomogeneous part.
```
### Expected behavior
Correctly construct FAISS DB without error.
|
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I_kwDOIPDwls5sVDA-
| 8,158 |
Error when importing ChatOutputParser: no validator found for <class 're.Pattern'>
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NONE
| null |
### System Info
from langchain.agents.chat.output_parser import ChatOutputParser
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ubuntu/chatpdf/Backend/backenv/lib/python3.10/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/home/ubuntu/chatpdf/Backend/backenv/lib/python3.10/site-packages/langchain/agents/__init__.py", line 10, in <module>
from langchain.agents.agent_toolkits import (
File "/home/ubuntu/chatpdf/Backend/backenv/lib/python3.10/site-packages/langchain/agents/agent_toolkits/__init__.py", line 6, in <module>
from langchain.agents.agent_toolkits.csv.base import create_csv_agent
File "/home/ubuntu/chatpdf/Backend/backenv/lib/python3.10/site-packages/langchain/agents/agent_toolkits/csv/base.py", line 4, in <module>
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
File "/home/ubuntu/chatpdf/Backend/backenv/lib/python3.10/site-packages/langchain/agents/agent_toolkits/pandas/base.py", line 18, in <module>
from langchain.agents.types import AgentType
File "/home/ubuntu/chatpdf/Backend/backenv/lib/python3.10/site-packages/langchain/agents/types.py", line 5, in <module>
from langchain.agents.chat.base import ChatAgent
File "/home/ubuntu/chatpdf/Backend/backenv/lib/python3.10/site-packages/langchain/agents/chat/base.py", line 6, in <module>
from langchain.agents.chat.output_parser import ChatOutputParser
File "/home/ubuntu/chatpdf/Backend/backenv/lib/python3.10/site-packages/langchain/agents/chat/output_parser.py", line 12, in <module>
class ChatOutputParser(AgentOutputParser):
File "pydantic/main.py", line 229, in pydantic.main.ModelMetaclass.__new__
File "pydantic/fields.py", line 491, in pydantic.fields.ModelField.infer
File "pydantic/fields.py", line 421, in pydantic.fields.ModelField.__init__
File "pydantic/fields.py", line 542, in pydantic.fields.ModelField.prepare
File "pydantic/fields.py", line 804, in pydantic.fields.ModelField.populate_validators
File "pydantic/validators.py", line 723, in find_validators
RuntimeError: no validator found for <class 're.Pattern'>, see `arbitrary_types_allowed` in Config
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents.chat.output_parser import ChatOutputParser
### Expected behavior
Import corrrectly.
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| 1,817,450,573 |
I_kwDOIPDwls5sVBhN
| 8,157 |
Retain links when extracting text using PDFLoader
|
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[] | null | 3 | 2023-07-24T01:53:00 | 2023-07-25T02:05:02 | 2023-07-25T02:05:01 |
NONE
| null |
### System Info
Hi,
I'm using version 0.0.105 of langchain (nodejs).
I'm using PDFLoader to embed PDF documents, and I've noticed that the extracted documents do not retain the hyperlinks that were present in the files.
Is there a way to retain them? I couldn't find it in the document or the PDFLoader class.
### Who can help?
@eyurtsev @hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
var pdfLoader = new PDFLoader(filePath, {
splitPages: false,
});
var rawDocs = await pdfLoader.load();
console.log(rawDocs);
### Expected behavior
I was expecting PDFLoader to convert the hyperlinks in the PDF to either raw URLs or markdown URL.
|
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| 1,817,442,875 |
I_kwDOIPDwls5sU_o7
| 8,156 |
create_tagging_chain returning an array string not respecting enum values
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[] | null | 2 | 2023-07-24T01:43:31 | 2023-12-18T23:48:57 | 2023-12-18T23:48:57 |
NONE
| null |
### System Info
Good day, I'm attempting to use `create_tagging_chain_pydantic` to tag certain markets wherein a product is used. The product can be used across multiple markets. Unfortunately the chain doesn't appear to respect the `enum` values passed to it for a return type of an array of string (it works correctly for a [single] string output).
```python
class Tags(BaseModel):
market_verticals: List[str] = Field(...,
enum=["pharmaceutical", "biotech", "food and beverage", "cosmetic",
"industrial", "nutraceuticals", "agriculture", "other"],
description="""describes which of the market verticals of the various uses
of the product
""")
def main():
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
chain = create_tagging_chain_pydantic(Tags, llm)
market_verticals = chain.run(
"The product is used in the production of food additives, pharmaceuticals, and cosmetics")
```
Output:
```bash
market_verticals=['food additives', 'pharmaceuticals', 'cosmetics']
```
Amending the prompt, produces the expected output:
```python
class Tags(BaseModel):
market_verticals: List[str] = Field(...,
enum=["pharmaceutical", "biotech", "food and beverage", "cosmetic",
"industrial", "nutraceuticals", "agriculture", "other"],
description="""describes which of the market verticals of the
various uses of the product. Market verticals are:
pharmaceutical, biotech, food and beverage, cosmetic, industrial,
nutraceuticals, agriculture and other.
""")
```
Output:
```bash
market_verticals=['food and beverage', 'pharmaceutical', 'cosmetic']
```
```bash
$ python3 -V
Python 3.10.10
$ pip show langchain
Name: langchain
Version: 0.0.240
Summary: Building applications with LLMs through composability
Home-page: https://www.github.com/hwchase17/langchain
Author:
Author-email:
License: MIT
Location: /home/tim/Documents/git/ai-magellan/.venv/lib/python3.10/site-packages
Requires: aiohttp, async-timeout, dataclasses-json, langsmith, numexpr, numpy, openapi-schema-pydantic, pydantic, PyYAML, requests, SQLAlchemy, tenacity
Required-by: kor
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Write and run the code below:
```
from typing import List
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.chains import create_tagging_chain, create_tagging_chain_pydantic
from langchain.prompts import ChatPromptTemplate
from enum import Enum
from pydantic import BaseModel, Field
class Tags(BaseModel):
# enum doesn't work with array
market_verticals: List[str] = Field(...,
items={"type": "string"}, enum=["pharmaceutical", "biotech", "food and beverage", "cosmetic",
"industrial", "nutraceuticals", "agriculture", "other"],
description="""describes which of the market verticals of the
various uses of the product
""")
def main():
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613", verbose=True)
chain = create_tagging_chain_pydantic(Tags, llm, verbose=True)
market_verticals = chain.run(
"Vitamin B is is used in the production of food additives, pharmaceuticals, and cosmetics")
print(market_verticals)
if __name__ == "__main__":
load_dotenv()
main()
```
### Expected behavior
Expected output:
```bash
market_verticals=['food and beverage', 'pharmaceuticals', 'cosmetics']
```
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PR_kwDOIPDwls5WLcob
| 8,155 |
nit: small typo in evaluation module docs
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[] | null | 1 | 2023-07-24T01:13:26 | 2023-07-24T01:25:15 | 2023-07-24T01:25:14 |
CONTRIBUTOR
| null |
Hopefully, this doesn't come across as nitpicky! That isn't the intention. I only noticed it, because I enjoy reading the documentation and when I hit a mental road bump it is usually due to a missing word or something =)
@baskaryan
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PR_kwDOIPDwls5WLZxL
| 8,154 |
Unbreak devcontainer
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[] | null | 1 | 2023-07-24T00:56:09 | 2023-07-24T02:33:47 | 2023-07-24T02:33:47 |
CONTRIBUTOR
| null |
Codespaces and devcontainer was broken by the [repo restructure](https://github.com/langchain-ai/langchain/discussions/8043).
- Description: Add libs/langchain to container so it can be built without error.
- Issue: -
- Dependencies: -
- Tag maintainer: @hwchase17 @baskaryan
- Twitter handle: @finnless
The failed build log says:
```
#10 [langchain-dev-dependencies 2/2] RUN poetry install --no-interaction --no-ansi --with dev,test,docs
#10 sha256:e850ee99fc966158bfd2d85e82b7c57244f47ecbb1462e75bd83b981a56a1929
2023-07-23 23:30:33.692Z: #10 0.827
#10 0.827 Directory libs/langchain does not exist
2023-07-23 23:30:33.738Z: #10 ERROR: executor failed running [/bin/sh -c poetry install --no-interaction --no-ansi --with dev,test,docs]: exit code: 1
```
The new pyproject.toml imports from libs/langchain:
https://github.com/langchain-ai/langchain/blob/77bf75c236351edf47d3a76a522bb45ccc90d299/pyproject.toml#L14-L16
But libs/langchain is never added to the dev.Dockerfile:
https://github.com/langchain-ai/langchain/blob/77bf75c236351edf47d3a76a522bb45ccc90d299/libs/langchain/dev.Dockerfile#L37-L39
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| 1,817,328,522 |
PR_kwDOIPDwls5WLKAz
| 8,153 |
Function calling logging fixup
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[] | null | 0 | 2023-07-23T22:48:31 | 2023-07-24T01:01:34 | 2023-07-24T01:01:33 |
COLLABORATOR
| null |
Fix bad overwriting of "functions" arg in invocation params.
Cleanup precedence in the dict
Clean up some inappropriate types (mapping should be dict)
Example: https://dev.smith.langchain.com/public/9a7a6817-1679-49d8-8775-c13916975aae/r
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| 1,817,287,908 |
PR_kwDOIPDwls5WLB18
| 8,152 |
simplified nmspace
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[] | null | 0 | 2023-07-23T20:34:03 | 2023-07-24T15:40:32 | 2023-07-24T01:02:20 |
COLLABORATOR
| null |
recreated #7894 (it is easy to recreate than resolve conflicts)
A small refactoring to improve the API Reference Agents table
@baskaryan
|
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PR_kwDOIPDwls5WK-t4
| 8,151 |
Optimize the cosine_similarity_top_k function performance
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[] | null | 4 | 2023-07-23T19:34:35 | 2023-07-27T01:03:50 | 2023-07-27T01:03:49 |
CONTRIBUTOR
| null |
Optimizing important numerical code and making it run faster.
Performance went up by 1.48x (148%). Runtime went down from 138715us to 56020us
Optimization explanation:
The `cosine_similarity_top_k` function is where we made the most significant optimizations.
Instead of sorting the entire score_array which needs considering all elements, `np.argpartition` is utilized to find the top_k largest scores indices, this operation has a time complexity of O(n), higher performance than sorting. Remember, `np.argpartition` doesn't guarantee the order of the values. So we need to use argsort() to get the indices that would sort our top-k values after partitioning, which is much more efficient because it only sorts the top-K elements, not the entire array. Then to get the row and column indices of sorted top_k scores in the original score array, we use `np.unravel_index`. This operation is more efficient and cleaner than a list comprehension.
The code has been tested for correctness by running the following snippet on both the original function and the optimized function and averaged over 5 times.
```
def test_cosine_similarity_top_k_large_matrices():
X = np.random.rand(1000, 1000)
Y = np.random.rand(1000, 1000)
top_k = 100
score_threshold = 0.5
gc.disable()
counter = time.perf_counter_ns()
return_value = cosine_similarity_top_k(X, Y, top_k, score_threshold)
duration = time.perf_counter_ns() - counter
gc.enable()
```
@hwaking @hwchase17 @jerwelborn
Unit tests pass, I also generated more regression tests which all passed.
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PR_kwDOIPDwls5WKu7B
| 8,150 |
bump experimental to 002
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[] | null | 1 | 2023-07-23T16:17:49 | 2023-07-24T01:28:29 | 2023-07-23T16:22:39 |
COLLABORATOR
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I_kwDOIPDwls5sT3ul
| 8,149 |
MultiRetrievalQAChain async mode
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[] | null | 1 | 2023-07-23T14:24:48 | 2023-10-29T16:04:26 | 2023-10-29T16:04:26 |
NONE
| null |
### Feature request
When I testing MultiRetrievalQAChain in synchronous mode, it worked perfectly. It can choose QA from the right retriever. But when I implement MultiRetrievalQAChain into production with streaming, I need mode async.
anyone can help me ?
TIA
### Motivation
I'm quite frustated when I found MultiRetrievalQAChain not yet support mode async. Because the launch time for the product is halted.
### Your contribution
yes, I'd love to help testing the new features from langchain.
|
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I_kwDOIPDwls5sTxx-
| 8,148 |
YoutubeLoader for nodejs
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[] | null | 2 | 2023-07-23T13:09:56 | 2023-11-01T16:06:00 | 2023-11-01T16:05:59 |
NONE
| null |
### Feature request
Would like to have YoutubeLoader on Nodejs api
https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.youtube.YoutubeLoader.html#langchain.document_loaders.youtube.YoutubeLoader
### Motivation
The motivation of this is to have the feature to load the transcript of youtube video and summarize it
### Your contribution
no
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PR_kwDOIPDwls5WKXFu
| 8,145 |
fix typo: Entity Summary Memory documentation
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[] | null | 3 | 2023-07-23T10:03:41 | 2023-07-24T02:36:51 | 2023-07-24T02:36:51 |
CONTRIBUTOR
| null |
Fixed a small typo I came across in the Memory documentation.
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PR_kwDOIPDwls5WKJRg
| 8,143 |
add faiss test for score threshold
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[] | null | 2 | 2023-07-23T05:34:21 | 2023-08-04T06:16:58 | 2023-07-24T02:36:38 |
CONTRIBUTOR
| null |
# What
- Add faiss vector search test for score threshold
- Fix failing faiss vector search test; filtering with list value is wrong.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add faiss vector search test for score threshold; Fix failing faiss vector search test; filtering with list value is wrong.
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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https://github.com/langchain-ai/langchain/issues/8142
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I_kwDOIPDwls5sTQx-
| 8,142 |
RecursiveCharacterTextSplitter overlap sometimes does not work
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[] | null | 5 | 2023-07-23T05:00:58 | 2023-11-08T23:32:08 | null |
NONE
| null |
### System Info
langchain 0.0.240
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Run the following code below:
```python
text = """14、中国证券报：今年以来，多个 数字人 民币试点地区开展了丰富多彩的数字人民币促消费活动，活跃消费市场，提振消费需求。不少地方政府向民众发放了数字人民币消费券，拉动居民消费，据记者粗略统计，活动金额超过1.8亿元。业内专家认为，数字人民币与消费券、电商平台等场景结合，可共同促进消费市场加快回暖。 15、猫眼专业版：电影《流浪地球2》上映15天，总票房已经破33亿。从综合票房看，《流浪地球2》再次登顶单日票房榜。2月5日单日实时票房超1.3亿元，领先第二名《满江红》近2000万，锁定单日票房冠军。目前，《流浪地球2》的票房占比34.5%，排片场次92126次。 二、市场回顾【国内股市】 1、2月6日，沪深两市三大指数弱势震荡，沪指跌0.76%， 深证成指 跌1.18%， 创业板 指跌1.4%。盘面上看， ChatGPT概念 持续火爆， 海天瑞声 、 云从科技 涨停6个交易日翻倍； 数字经济 概念盘中发酵， 铜牛信息 涨停；信创、软件股逆市走强， 青 云科技 涨停；八部门适度超前建设充换电基础设施， 充电桩 概念活跃。 白酒 、CRO、新冠 疫苗 、 券商 、 黄金 有色、 保险 、 磷化工 等板块跌幅居前。 2、港股低开低走，截至收盘， 恒生指数 跌2.02%，报21222.16点； 国企指数 跌2.68%， 红筹指数 跌1.37%。 恒生科技指数 跌3.65%，万国数据跌8.65%，哔哩哔哩跌5.38%，快手跌5.05%。 黄金股 集体走低， 山东黄金 跌6.01%， 紫金矿业 跌超5%， 招金矿业 跌4.56%。汽车股下跌， 长城汽车 跌超6%，蔚来跌超5%， 零跑汽车 跌4.80%。 3、年2月6日， 大智慧 数据中心显示，龙虎榜中营业部净卖出3616.66万元。其中，净买入的个股24只，营业部净买入较多个股分别是 润和软件 、 信达证券 、 川大智胜 等，净买入金额占当日成交额比例达2.95%、80.60%、7.19%。净卖出的个股23只，营业部净卖出居前个股分别为 赛为智能 、 同为股份 、 万里石 等，净卖出金额占当日成交额比例达4.92%、4.32%、11.78%。 龙虎榜中机构净卖出1.64亿元。其中，净买入的个股8只，机构净买入较多个股分别是同为股份、 飞龙股份 、赛为智能等，净买入金额占当日成交额比例达0.82%、1.35%、0.48%。净卖出的个股19只，机构净卖出居前个股分别为润和软件、 博睿数据 、 宏景科技 等，净卖出金额占当日成交额比例达0.70%、12.88%、4.76%。 4、北向资金2月6日小幅净卖出5.43亿。其中净买入第一位 贵州茅台 5.46亿、 宁德时代 4.69亿紧随其后，净卖出 万科A 3.87亿、 科大讯飞 2.18亿。南向资金今日净流入19.90亿港元。港股通（沪）方面，恒生中国企业、盈富基金净买入居前，分别获净买入7.77亿港元、5.78亿港元。港股通（深）方面，盈富基金、恒生中国企业净买入居前，分别获净买入25.00亿港元、8.67亿港元。 【环球市场】 1、美股：北京时间7日凌晨，美股周一录得连续第二个交易日收跌。美国国债收益率连续两日大幅攀升令市场情绪紧张。投资者关注美股财报，并等待美联储主席鲍威尔明天将发表的重要讲话。 标普500 指数收跌25.40点，跌幅0.61%，报4111.08点。道指收跌34.99点，跌幅0.10%，报33891.02点。纳指收跌119.50点，跌幅1.00%，报11887.45点。 2、欧股：德国DAX 30指数收跌0.84%，报15345.91点。法国CAC 40指数收跌1.34%，报7137.10点。英国富时100指数收跌0.82%，报7836.71点。 3、黄金：COMEX 4月黄金期货收涨0.15%，报1879.50美元/盎司。 4、 原油 ：WTI 3月原油期货收涨0.72美元，涨幅0.98%，报74.11美元/桶。布伦特4月原油期货收涨1.05美元，涨幅1.31%，报80.99美元/桶。 【机构策略】 对于近日市场走势，银河证券指出，节后市场短期波动原因包括：1)春节前市场情绪高涨，第一波上涨实现后，投资者转而更加关注基本面是否能够兑现，市场出现震荡。2)北向资金节前大幅净流入，但对比美国经济复苏初期，我国目前直接促消费措施力度有限，叠加近期上市公司业绩逐步披露兑现，经济修复仍有部分不确定性因素存在，或与外资对我国经济复苏的节奏判断产生预期差，节后也可以看到北向资金流入短暂放缓，增量资金的支持减弱。3)从已披露2022年报业绩预告来看，受去年四季度受疫情冲击拖累，部分企业开工及居民消费都受到较大影响，预计2022年年报业绩仍处底部区间。4)外围市场来看，美国就业数据大超预期引发投资者对美联储加息预期产生分歧，短期引发市场担忧。 该机构认为，虽目前市场短期有一定波动，但中期中国经济企稳增长预期将不断兑现、市场流动性充足支撑、盈利面逐步回暖等对市场形成支撑。同时春节并未带来二次疫情高峰使内资对疫情反复的担忧下降，未来内资或将逐步接棒北向资金。从风格角度看，2022年11月以来，市场春季躁动效应演绎充分，大盘股长势较好。从1月份以来， 中小盘 股开始逐渐举起，行业也从消费切换到科技，风格切换明显，我们认为短期 小盘成长 风格或延续占优：一是成长板块，尤其是TMT板块，整体估值处于历史较低位置，安全垫高，反弹动力强。二是从资金流动的视角，消费板块在经历快速上涨后，目前股价有所回落，成长板块仍是资金配置的最优选择。三是主题风格的演绎催化带动成长板块的上行，行业景气度持续提升，如近期ChatGPT的快速突破也使得 人工智能 产业链备受投资者青睐，未来发展空间广阔。值得注意的是，目前成长板块上涨较快，短期可能面临交易过热或技术位调整的风险，但整体来看，小盘风格占优的趋势仍将延续。四是从投资日历角度来看，2月科技板块胜率较高。 【新股申购】 一致魔芋证券代码839273，发行价11.38元，发行市盈率15.98倍，主营魔芋及魔芋相关产品。 扬州金泉证券代码603307，发行价31.04元，发行市盈率21.61倍，主营帐篷和睡袋。 坤泰股份证券代码001260，发行价14.27元，发行市盈率22.98倍，主营汽车地毯、脚垫。 三、公司公告和预警 【业绩】 通化东宝：业绩快报2022年净利同比增21.46% 富煌钢构：2022年累计新签销售合同额约49.8亿元 同比下降11% 新城控股：1月实现合同销售金额约57.76亿元 比上年同期减少26.54% 金地商置：1月合约销售约22.96亿元 同比降59.68% 融信中国：1月合约销售约9.95亿元 同比降84.7% 新城发展：1月合约销售金额约57.76亿元 同比降24.81% 华安证券：业绩快报2022年实现净利11.81亿元 同比降17% 恒投证券：预计2022年度股东应占亏损约11.59亿元 同比盈转亏 保利置业：1月合同销售金额约49亿元 同比增长105% 融创中国：1月合同销售约72.3亿元 同比降74.1% 【重大事项】 永鼎股份：全资子公司获得12.4亿元线束业务中标确认 康跃科技：拟将证券简称变更为“长药控股” 壹网壹创：公司目前已部署AI内容分析 奥飞数据：公司为百度AI业务提供算力服务支持 晶方科技：“MEMS 传感器 芯片先进封装测试平台”项目获得立项批复 合兴股份：拟公开发行可转债募资不超6.1亿元 风语筑：公司已结合AIGC技术在文生文、文生图、文生音视频等领域进行场景应用 保利发展：拟打包转让12个与 碧桂园 合作项目公司股权 大族激光：目前接近式 光刻机 已投入市场 凯立新材：拟定增募资不超过10.75亿元 宇通客车：宇通集团等拟要约收购公司股份 要约价为7.89元/股 【增减持】 天通股份：公司拟减持 亚光科技 不超1%股份 隆华新材：新余隆振拟减持不超0.88% 海程邦达：股东及董监高拟减持不超1.91% ST开元：实控人拟减持公司不超1.4%股份 沃尔德：实际控制人及一致行动人拟合计减持不超4.01% 海南高速：拟减持不超 海汽集团 3%股份 马云减持约3194万港元，虞锋减持约5728万港元腾盛博药 神州数码：股东王晓岩拟减持不超2%股份 友邦保险：回购263.08万股 耗资约2.25亿港元 【预警】 川能动力：公司董事长被立案调查 云从科技：未与OpenAI开展合作 ChatGPT的产品和服务未给公司带来业务收入 海天瑞声：尚未与OpenAI开展合作 ChatGPT产品和服务尚未给公司带来业务收入 新国都：子公司业务暂不涉及ChatGPT文本生成式AIGC领域 ST三圣：公司及控股股东潘先文遭证监会立案 """
import os
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n", "。", " "], keep_separator=False, chunk_size=256, chunk_overlap=64)
docs = text_splitter.create_documents([text])
docs
```
### Expected behavior
BUG: I get 19 documents. For example, let's see the 2nd and 3rd document. They actually don't have any overlaps. They are exactly cut by `。` in the middle (which I mark it with `||` ) : `2月6日，沪深两市三大指数弱势震荡，沪指跌0.76%， 深证成指 跌1.18%， 创业板 指跌1.4%` `||` `。` `||` `盘面上看， ChatGPT概念 持续火爆， 海天瑞声 、 云从科技 涨停6个交易日翻倍； ` . I put the example below:
1st: ... is ok, have a trunc of overlap
2nd: `2月5日单日实时票房超1.3亿元，领先第二名《满江红》近2000万，锁定单日票房冠军。目前，《流浪地球2》的票房占比34.5%，排片场次92126次。 二、市场回顾【国内股市】 1、2月6日，沪深两市三大指数弱势震荡，沪指跌0.76%， 深证成指 跌1.18%， 创业板 指跌1.4%`
3rd: `盘面上看， ChatGPT概念 持续火爆， 海天瑞声 、 云从科技 涨停6个交易日翻倍； 数字经济 概念盘中发酵， 铜牛信息 涨停；信创、软件股逆市走强， 青 云科技 涨停；八部门适度超前建设充换电基础设施， 充电桩 概念活跃。 白酒 、CRO、新冠 疫苗 、 券商 、 黄金 有色、 保险 、 磷化工 等板块跌幅居前。 2、港股低开低走，截至收盘， 恒生指数 跌2.02%，报21222.16点； 国企指数 跌2.68%， 红筹指数 跌1.37%`
There are also a lot of non overlapped documents in these 19 splitted documents. Please have a lot. Thanks!
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I_kwDOIPDwls5sTOJ3
| 8,141 |
Issue: Why condense the conversational history when Open AI provides a 'messages' field in its API?
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[] | null | 3 | 2023-07-23T04:01:03 | 2023-10-23T22:41:46 | 2023-10-23T22:41:37 |
NONE
| null |
### Issue you'd like to raise.
Hi there,
I've found many scenarios where ConversationalRetrievalChain's attempt to condense a conversation history into a stand-alone question fails, either because the history is too complex or, for example, if the user simply provides an affirmation.
Why do we attempt to condense the conversational history when Open AI's chat API provides a [messages](https://platform.openai.com/docs/api-reference/chat/create#chat/create-messages) field for this purpose?
Is there a way to construct a retrieval chain such that the messages field in Open AI's chat API can be used instead of attempting to condense the chat history into a stand-alone question?
### Suggestion:
Provide a way to construct a retrieval chain such that Open AI's chat API's messages field can be used instead of attempting to condense the question.
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| 1,816,959,325 |
PR_kwDOIPDwls5WKCbX
| 8,140 |
add llamaapi
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[] | null | 1 | 2023-07-23T02:16:14 | 2023-07-23T16:16:18 | 2023-07-23T16:16:17 |
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| 1,816,953,626 |
PR_kwDOIPDwls5WKBTR
| 8,139 |
beef up getting started
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[] | null | 2 | 2023-07-23T01:48:42 | 2023-07-24T02:57:45 | 2023-07-24T02:57:44 |
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| 1,816,916,762 |
PR_kwDOIPDwls5WJ6AQ
| 8,138 |
Harrison/agent intro
|
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[] | null | 1 | 2023-07-22T22:44:13 | 2023-07-23T05:15:01 | 2023-07-23T05:15:00 |
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| 1,816,905,842 |
PR_kwDOIPDwls5WJ37x
| 8,137 |
fix-typo
|
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[] | null | 1 | 2023-07-22T21:52:14 | 2023-07-24T01:02:58 | 2023-07-24T01:02:58 |
COLLABORATOR
| null |
more general questions about this function:
```
return {
**super()._get_invocation_params(stop=stop, **kwargs),
**self._default_params,
"model": self.model_name,
"functions": kwargs.get("functions"),
}
```
1. `**super()._get_invocation_params(stop=stop, **kwargs)` returns everything in kwargs, including functions (if present). do we do `"functions": kwargs.get("functions"),` because we explicitly need to pass functions through even when not present? (im assuming this is not needed (because its been broken and we havent noticed) so can we just remove
2. This ordering of
```
**super()._get_invocation_params(stop=stop, **kwargs),
**self._default_params,
```
means that if an arbitrary param is passed in through `kwargs` that is same as one in `**self._default_params,`, the one in defuaut params gets logged. imo we want to give preference to everything in kwargs right? why is this ordering the same?
|
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| 1,816,901,883 |
PR_kwDOIPDwls5WJ3MN
| 8,136 |
actually use input key
|
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[] | null | 1 | 2023-07-22T21:35:08 | 2023-07-24T01:02:46 | 2023-07-24T01:02:45 |
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| 1,816,890,724 |
PR_kwDOIPDwls5WJ1BA
| 8,135 |
Feature faiss delete
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[] | null | 4 | 2023-07-22T20:51:49 | 2023-08-10T03:13:10 | 2023-08-06T22:46:30 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: docstore had two main method: add and search, however, dealing with docstore sometimes requires deleting an entry from docstore. So I have added a simple delete method that deletes items from docstore. Additionally, I have added the delete method to faiss vectorstore for the very same reason.
- Issue: NA
- Dependencies: NA
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5WJrvL
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Add embeddings for LocalAI
|
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| false | null |
[] | null | 3 | 2023-07-22T17:35:19 | 2023-07-25T18:31:01 | 2023-07-24T19:16:49 |
CONTRIBUTOR
| null |
Description:
This PR adds embeddings for LocalAI ( https://github.com/go-skynet/LocalAI ), a self-hosted OpenAI drop-in replacement. As LocalAI can re-use OpenAI clients it is mostly following the lines of the OpenAI embeddings, however when embedding documents, it just uses string instead of sending tokens as sending tokens is best-effort depending on the model being used in LocalAI. Sending tokens is also tricky as token id's can mismatch with the model - so it's safer to just send strings in this case.
Partly related to: https://github.com/hwchase17/langchain/issues/5256
Dependencies: No new dependencies
Twitter: @mudler_it
Maintainers: @rlancemartin, @eyurtsev, @hwchase17
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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PR_kwDOIPDwls5WJrhl
| 8,133 |
doc(sqldatabasechain): use views when jsonb column description is not available
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[] | null | 1 | 2023-07-22T17:30:47 | 2023-07-22T18:30:05 | 2023-07-22T18:30:05 |
CONTRIBUTOR
| null |
I think the PR diff is self explaining ;)
@baskaryan
|
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I_kwDOIPDwls5sSocE
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create_sql_aqent not working with `AgentType.OPENAI_FUNCTIONS`
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[] | null | 5 | 2023-07-22T16:35:34 | 2023-10-29T16:04:36 | 2023-10-29T16:04:36 |
NONE
| null |
### System Info
Langchain version: ```langchain = "^0.0.236"````
Python 3.10.10
MacOSx 13.4.1 (c) (22F770820d)
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Mysql 5.7 db.
```
toolkit = SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0))
agent_executor = create_sql_agent(
llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),
toolkit=toolkit,
verbose=False,
agent_type=AgentType.OPENAI_FUNCTIONS,
)
response = agent_executor.run(question)
```
Error message:
```none is not an allowed value (type=type_error.none.not_allowed)```
Note: It does work with `AgentType.ZERO_SHOT_REACT_DESCRIPTION`
### Expected behavior
I would expect a response from the agent
Observed:
```none is not an allowed value (type=type_error.none.not_allowed)```
|
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PR_kwDOIPDwls5WJnw9
| 8,131 |
Create langflow
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[] | null | 2 | 2023-07-22T16:28:45 | 2023-07-22T18:28:27 | 2023-07-22T18:28:27 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,816,809,973 |
PR_kwDOIPDwls5WJlkC
| 8,130 |
edit prompt format method
|
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[] | null | 2 | 2023-07-22T15:53:53 | 2023-07-22T18:32:46 | 2023-07-22T18:32:45 |
CONTRIBUTOR
| null |
- Description: Modify the `PromptTemplate`'s `format` method behavior by temporarily altering the `__repr__` and `__format__` methods of the `Document` class. This will result in only the `page_content` being added to the template when conduct formatting. Refer to the unit test case below for further clarification.
- Issue: the issue #7967 it fixes
- Dependencies: No
- Tag maintainer: @baskaryan @rlancemartin
- Twitter handle: dayuanjian21687
Below is the unit test cases.
```python
prompt = PromptTemplate.from_template("This is a {var} test.")
# if the variable is string
output = prompt.format(var="good")
assert output == "This is a good test."
# if the variable is Document without meta_data
doc = Document(page_content="good")
output = prompt.format(var=doc)
print(output)
assert output == "This is a 'good' test."
# if the variable is Document with meta_data
doc = Document(page_content="good", meta_data={"bar": "baz"})
output = prompt.format(var=doc)
assert output == "This is a 'good' test."
# if the variable is Document and it is inside a list
doc1 = Document(page_content="good1", meta_data={"bar": "baz"})
doc2 = Document(page_content="good2", meta_data={"bar": "baz"})
output = prompt.format(var=[doc1, doc2])
assert output == "This is a ['good1', 'good2'] test."
```
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| 1,816,803,502 |
PR_kwDOIPDwls5WJkTd
| 8,129 |
bump versions
|
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[] | null | 1 | 2023-07-22T15:32:35 | 2023-07-22T15:46:38 | 2023-07-22T15:46:37 |
COLLABORATOR
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| 1,816,799,265 |
PR_kwDOIPDwls5WJjfX
| 8,128 |
Harrison/add back in experimental
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[] | null | 1 | 2023-07-22T15:18:31 | 2023-07-22T15:27:30 | 2023-07-22T15:27:29 |
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PR_kwDOIPDwls5WJhfB
| 8,127 |
fix empty ids when metadatas is provided
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CONTRIBUTOR
| null |
Fixes https://github.com/hwchase17/langchain/issues/7865 and https://github.com/hwchase17/langchain/issues/8061
- [x] fixes returning empty ids when metadatas argument is provided
@baskaryan
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I_kwDOIPDwls5sSdfH
| 8,126 |
Issue: Using qdrant.as_retriver() to use the filtered data of a collection (filtered on the basis of payload data)
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[] | null | 5 | 2023-07-22T14:09:54 | 2023-11-03T16:06:32 | 2023-11-03T16:06:31 |
NONE
| null |
### Issue you'd like to raise.
Unable to use metadata filtering of a collection qdrant and then use it in as_retriever function.
### Suggestion:
Qdrant.from_documents should include a filtering method as well to filer on the collection with payload (metadata)
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| 1,816,770,103 |
I_kwDOIPDwls5sSbY3
| 8,125 |
call directly the prompt of GPT4All Model
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[] | null | 2 | 2023-07-22T13:43:02 | 2023-07-25T08:29:19 | 2023-07-25T08:29:19 |
NONE
| null |
### System Info
Langchain 0.0.238
System: WSL - Ubuntu
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use the ConversationBufferMemory and Prompt Templates.
if you have the template
```python
"""
Human: Ask question
Assistant:
"""
```
The prompt passed to GPT4AllModel is again templated by gpt4all.py to avoid which results in the following prompt which is passed to the model
```python
"""
### Human Human: Ask question
Assistant:
### Assistant:
"""
```
This leads to incorrect answers where models are trying to talk to them self as human and assistant
if we directly call GPT4AllModel.model.prompt_model_streaming we are giving the prompt the user indented.
### Expected behavior
That the prompt Template is used that the user/developer gives the Chain
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PR_kwDOIPDwls5WJcML
| 8,124 |
Add batch processing and wait time to FAISS from_texts method
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[] | null | 6 | 2023-07-22T13:13:27 | 2023-08-05T11:22:51 | 2023-07-22T18:52:37 |
NONE
| null |
Description: This pull request introduces two new optional parameters to the from_texts method in the FAISS class: batch_size and wait_time. When both parameters are provided, the method will process the texts in batches, waiting for a specified amount of time between each batch. This can be useful when dealing with rate-limited APIs such as the OpenAI API. If tqdm is not installed, an ImportError will be raised.
Issue: This change addresses the issue of handling large volumes of text processing when using rate-limited APIs (solution to issue #634).
Dependencies: This change requires the tqdm module.
Tag maintainer: @rlancemartin, @eyurtsev (as this change is related to VectorStores)
Twitter handle: @syusuke7777
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I_kwDOIPDwls5sSReD
| 8,123 |
Issue: Rewrite SelfQueryRetriever llm prompt for better results
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[] | null | 1 | 2023-07-22T11:23:10 | 2023-10-28T16:04:20 | 2023-10-28T16:04:19 |
NONE
| null |
### Issue you'd like to raise.
How I can change the value of "retriever.llm_chain.prompt" accordingly for specific task to achieve better and accurate results as self Query retrieval isn't working with Open source LLMs it raises following error and there is an issue related to this error that's not yet resolved as well.
OutputParserException: Parsing text
{
"query": "simon josu",
"filter": "and(eq(\"from\", \"Simon assistant app Josu\"), geq(\"date_received\", \"2020-12-14T00:00:00Z\"))"
}
raised following error:
Received unrecognized function geq. Valid functions are [<Operator.AND: 'and'>, <Operator.OR: 'or'>, <Operator.NOT: 'not'>, <Comparator.EQ: 'eq'>, <Comparator.GT: 'gt'>, <Comparator.GTE: 'gte'>, <Comparator.LT: 'lt'>, <Comparator.LTE: 'lte'>]
### Suggestion:
_No response_
|
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| 1,816,718,804 |
I_kwDOIPDwls5sSO3U
| 8,122 |
missing parameter in similarity_search function in azuresearch.py
|
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[] | null | 2 | 2023-07-22T10:43:30 | 2023-10-28T16:04:25 | 2023-10-28T16:04:24 |
NONE
| null |
### System Info
at line 250, self.semantic_hybrid_search function only pass two parameters, query and k. it missing "**kwargs "
241 def similarity_search(
242 self, query: str, k: int = 4, **kwargs: Any
243 ) -> List[Document]:
244 search_type = kwargs.get("search_type", self.search_type)
245 if search_type == "similarity":
246 docs = self.vector_search(query, k=k)
247 elif search_type == "hybrid":
248 docs = self.hybrid_search(query, k=k)
249 elif search_type == "semantic_hybrid":
250 # docs = self.semantic_hybrid_search(query, k=k)
251 docs = self.semantic_hybrid_search(query, k=k ,**kwargs )
252 else:
253 raise ValueError(f"search_type of {search_type} not allowed.")
254 return docs
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
without passing "**kwargs ", filter won't work
### Expected behavior
docs = vector_store.similarity_search(
query=query,
k=3,
search_type="semantic_hybrid",
searchFields='content_vector',
filters="mrn eq '04c3a6bcfcc821d7b7ac722713f4bf7c'",
)
return doc with filtered by filter condition
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PR_kwDOIPDwls5WJS2t
| 8,121 |
GitHub toolkit improvements
|
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[] | null | 2 | 2023-07-22T10:24:44 | 2023-07-24T03:17:54 | 2023-07-24T03:17:54 |
CONTRIBUTOR
| null |
Fixes an issue with the github tool where the API returned special objects but the tool was expecting dictionaries.
Also added proper docstrings to the GitHubAPIWraper methods and a (very basic) integration test.
Maintainer responsibilities:
- Agents / Tools / Toolkits: @hinthornw
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PR_kwDOIPDwls5WJPZO
| 8,120 |
add HuggingGPT Agent
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[] | null | 3 | 2023-07-22T09:16:39 | 2023-07-24T04:55:54 | 2023-07-24T04:55:53 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
- Description: add HuggingGPT Agent, see the [paper](https://arxiv.org/abs/2303.17580) and the [original project](https://github.com/microsoft/JARVIS).
- Notebook: add a [notebook](https://github.com/tricktreat/langchain/blob/bc840fe4307690505b3c0b051e0e2d9726777437/docs/extras/use_cases/autonomous_agents/hugginggpt.ipynb) showing its use.
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| 1,816,667,727 |
PR_kwDOIPDwls5WJKit
| 8,119 |
Add default K in Retriever
|
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[] | null | 7 | 2023-07-22T07:44:20 | 2023-08-04T05:52:02 | 2023-08-04T05:52:02 |
NONE
| null |
- Description: add default class variable for inheriting BaseRetriever
- Issue: issue #7922 ,
- Dependencies: no,
- Tag maintainer: @rlancemartin, @eyurtsev
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| 1,816,637,467 |
PR_kwDOIPDwls5WJFOq
| 8,117 |
adding full path to .toml files for the COPY in the dev dockerfile
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[] | null | 2 | 2023-07-22T05:57:26 | 2023-07-26T00:38:43 | 2023-07-26T00:38:42 |
CONTRIBUTOR
| null |
- Description: in the .devcontainer, docker-compose build is currently failing due to the src paths in the COPY command. This change adds the full path to the pyproject.toml and poetry.toml to allow the build to run.
- Issue: na
- Dependencies: none
- Twitter handle: byronsalty
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PR_kwDOIPDwls5WJDxt
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add tests to redis vectorstore
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[] | null | 2 | 2023-07-22T05:22:39 | 2023-08-04T06:16:38 | 2023-08-01T00:07:09 |
CONTRIBUTOR
| null |
# What
- Add function to get similarity with score with threshold in Redis vector store.
- Add tests to Redis vector store.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add tests to Redis vector store.
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5WI-om
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Generalize Comment on Streaming Support for LLM Implementations and add examples
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[] | null | 3 | 2023-07-22T04:27:42 | 2023-07-24T06:02:26 | 2023-07-24T06:00:00 |
CONTRIBUTOR
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The example provided demonstrates the usage of the HuggingFaceTextGenInference implementation with streaming enabled.
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changed to mlflow-ai-gateway in llms/__init__.py
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[] | null | 2 | 2023-07-22T03:00:44 | 2023-07-24T06:30:46 | 2023-07-24T06:30:46 |
CONTRIBUTOR
| null |
- Description: In the llms/__init__.py, the key name is wrong for mlflowaigateway. It should be mlflow-ai-gateway
- Issue: NA
- Dependencies: NA
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: na
Without this fix, when we run the code for mlflowaigateway, we will get error as below
ValueError: Loading mlflow-ai-gateway LLM not supported
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PR_kwDOIPDwls5WI25A
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add filter to sklearn vector store functions
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[] | null | 1 | 2023-07-22T02:54:32 | 2023-08-04T06:16:27 | 2023-08-04T06:06:41 |
CONTRIBUTOR
| null |
# What
- This is to add filter option to sklearn vectore store functions
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add filter to sklearn vectore store functions.
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5WIzO6
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add save and load tfidf vectorizer and docs for TFIDFRetriever
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[] | null | 1 | 2023-07-22T02:26:41 | 2023-08-04T06:06:27 | 2023-08-04T06:06:27 |
CONTRIBUTOR
| null |
This is to add save_local and load_local to tfidf_vectorizer and docs in tfidf_retriever to make the vectorizer reusable.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add save_local and load_local to tfidf_vectorizer and docs in tfidf_retriever
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5WIxWQ
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Save tfidf vectorizer and docs in TFIDFRetriever
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[] | null | 1 | 2023-07-22T02:16:44 | 2023-07-22T02:22:05 | 2023-07-22T02:22:03 |
CONTRIBUTOR
| null |
This is to add save_local and load_local to tfidf_vectorizer and docs in tfidf_retriever to make the vectorizer reusable.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add save_local and load_local to tfidf_vectorizer and docs in tfidf_retriever
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5WIxOh
| 8,110 |
MultiOn client toolkit
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[] | null | 1 | 2023-07-22T02:14:15 | 2023-07-22T15:19:02 | 2023-07-22T15:19:02 |
CONTRIBUTOR
| null |
Addition of MultiOn Client Agent Toolkit
Dependencies: multion pip package
This PR consists of the following:
- MultiOn utility,tools and integration with agent
- sample jupyter notebook.
Request @hwchase17 , @hinthornw
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PR_kwDOIPDwls5WIwsW
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Save TFIDF Vectorizer in tfidf_retriever.py
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[] | null | 1 | 2023-07-22T02:03:38 | 2023-07-22T02:08:18 | 2023-07-22T02:08:14 |
CONTRIBUTOR
| null |
This is to add save_local and load_local to tfidf_vectorizer and docs in tfidf_retriever to make the vectorizer reusable.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add save_local and load_local to tfidf_vectorizer and docs in tfidf_retriever
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5WIwU7
| 8,108 |
add Hybrid retriever that not require any external service
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[] | null | 5 | 2023-07-22T01:56:17 | 2023-07-25T02:26:36 | 2023-07-25T02:16:11 |
CONTRIBUTOR
| null |
- Until now, hybrid search was limited to modules requiring external services, such as Weaviate/Pinecone Hybrid Search. However, I have developed a hybrid retriever that can merge a list of retrievers using the [Reciprocal Rank Fusion](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) algorithm. This new approach, similar to Weaviate hybrid search, does not require the initialization of any external service.
- Dependencies: No
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: dayuanjian21687
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PR_kwDOIPDwls5WIwHC
| 8,107 |
Added stop sequence support to replicate
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[] | null | 3 | 2023-07-22T01:51:51 | 2023-07-25T02:58:58 | 2023-07-25T00:34:13 |
CONTRIBUTOR
| null |
Stop sequences are useful if you are doing long-running completions and need to early-out rather than running for the full max_length... not only does this save inference cost on Replicate, it is also much faster if you are going to truncate the output later anyway.
Other LLMs support stop sequences natively (e.g. OpenAI) but I didn't see this for Replicate so adding this via their prediction cancel method.
Housekeeping: I ran `make format` and `make lint`, no issues reported in the files I touched.
I did update the replicate integration test and ran `poetry run pytest tests/integration_tests/llms/test_replicate.py` successfully.
Finally, I am @tjaffri https://twitter.com/tjaffri for feature announcement tweets... or if you could please tag @docugami https://twitter.com/docugami we would really appreciate that :-)
Tagging model maintainers @hwchase17 @baskaryan
Thank for all the awesome work you folks are doing.
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PR_kwDOIPDwls5WIvdZ
| 8,106 |
Harrison/official pre release
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[] | null | 1 | 2023-07-22T01:38:31 | 2023-07-22T01:44:33 | 2023-07-22T01:44:32 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5sRe4q
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DOC: Link broken for Python Guide on Prompts Page
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[] | null | 3 | 2023-07-22T01:09:31 | 2023-07-31T09:12:19 | 2023-07-31T09:12:19 |
NONE
| null |
### Issue with current documentation:
The hyperlink which should navigate to the Python guide for Prompts seems to be broken.
Affected link -> https://docs.langchain.com/docs/components/prompts/
Click on the `Python Guide` link under the `info` section. It leads to `Page Not Found`.
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5WItfY
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Enhance Qdrant VS performances by using async embed documents
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[] | null | 4 | 2023-07-22T01:02:07 | 2023-08-19T02:00:07 | 2023-08-19T02:00:06 |
CONTRIBUTOR
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- Description: Since OpenAI supports async embed documents then the Qdrant VS (and other as well but I focus on Qdrant for now) should use it as well to avoid blocking IO when creating embedded data
- Dependencies: No
- Tag maintainer: @agola11
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Update SageMaker Endpoint Embeddings docs to be up to date with current requirements
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[] | null | 2 | 2023-07-22T00:14:10 | 2023-07-24T20:35:07 | 2023-07-24T20:35:06 |
CONTRIBUTOR
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- **Description:** Simple change of the Class that ContentHandler inherits from. To create an object of type SagemakerEndpointEmbeddings, the property content_handler must be of type EmbeddingsContentHandler not ContentHandlerBase anymore,
- **Maintainer:** @baskaryan
- **Twitter handle:** @Juanjo_Torres11
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PR_kwDOIPDwls5WIlNd
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Web research retriever
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[] | null | 2 | 2023-07-21T23:48:04 | 2023-07-26T02:58:01 | 2023-07-26T02:58:01 |
COLLABORATOR
| null |
Given a user question, this will -
* Use LLM to generate a set of queries.
* Query for each.
* The URLs from search results are stored in self.urls.
* A check is performed for any new URLs that haven't been processed yet (not in self.url_database).
* Only these new URLs are loaded, transformed, and added to the vectorstore.
* The vectorstore is queried for relevant documents based on the questions generated by the LLM.
* Only unique documents are returned as the final result.
This code will avoid reprocessing of URLs across multiple runs of similar queries, which should improve the performance of the retriever. It also keeps track of all URLs that have been processed, which could be useful for debugging or understanding the retriever's behavior.
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PR_kwDOIPDwls5WIZFE
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mv module integrations docs
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[] | null | 1 | 2023-07-21T21:59:22 | 2023-07-24T06:23:17 | 2023-07-24T06:23:16 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5WIVC8
| 8,100 |
SQL Query Prompt update + added _execute method for SQLDatabase
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[] | null | 5 | 2023-07-21T21:33:54 | 2023-08-01T07:53:01 | 2023-07-31T23:28:08 |
CONTRIBUTOR
| null |
- Description: This pull request (PR) includes two minor changes:
1. Updated the default prompt for SQL Query Checker: The current prompt does not clearly specify the final response that the LLM (Language Model) should provide when checking for the query if `use_query_checker` is enabled in SQLDatabase Chain. As a result, the LLM adds extra words like "Here is your updated query" to the response. However, this causes a syntax error when executing the SQL command in SQLDatabaseChain, as these additional words are also included in the SQL query.
2. Moved the query's execution part into a separate method for SQLDatabase: The purpose of this change is to provide users with more flexibility when obtaining the result of an SQL query in the original form returned by sqlalchemy. In the previous implementation, the run method returned the results as a string. By creating a distinct method for execution, users can now receive the results in original format, which proves helpful in various scenarios. For example, during the development of a tool, I found it advantageous to obtain results in original format rather than a string, as currently done by the run method.
- Tag maintainer: @hinthornw
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PR_kwDOIPDwls5WIT7m
| 8,099 |
Refactored `example_generator`
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[] | null | 2 | 2023-07-21T21:27:53 | 2023-07-24T20:53:53 | 2023-07-24T20:36:45 |
COLLABORATOR
| null |
Refactored `example_generator.py`. The same as #7961
`example_generator.py` is in the root code folder. This creates the `langchain.example_generator: Example Generator ` group on the API Reference navigation ToC, on the same level as `Chains` and `Agents` which is not correct.
Refactoring:
- moved `example_generator.py` content into `chains/example_generator.py` (not in `utils` because the `example_generator` has dependencies on other LangChain classes. It also doesn't work for moving into `utilities/`)
- added the backwards compatibility ref in the original `example_generator.py`
@hwchase17
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| 1,816,374,869 |
PR_kwDOIPDwls5WIODO
| 8,098 |
Refactored `env`
|
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[] | null | 2 | 2023-07-21T20:56:17 | 2023-07-24T20:52:55 | 2023-07-24T20:52:55 |
COLLABORATOR
| null |
Refactored `env.py`. The same as #7961
`env.py` is in the root code folder. This creates the `langchain.env: Env ` group on the API Reference navigation ToC, on the same level as `Chains` and `Agents` which is not correct.
Refactoring:
- moved env.py content into utils/env.py
- added the backwards compatibility ref in the original `env.py`
@hwchase17
|
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PR_kwDOIPDwls5WIK0_
| 8,097 |
bump releases rc0
|
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[] | null | 1 | 2023-07-21T20:39:50 | 2023-07-21T20:54:58 | 2023-07-21T20:54:58 |
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PR_kwDOIPDwls5WIJ8H
| 8,096 |
add experimental release action
|
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[] | null | 1 | 2023-07-21T20:35:10 | 2023-07-21T20:38:36 | 2023-07-21T20:38:35 |
COLLABORATOR
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| 1,816,308,692 |
I_kwDOIPDwls5sQqvU
| 8,095 |
Human Tool with websocket
|
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[] | null | 5 | 2023-07-21T19:47:10 | 2023-11-06T18:11:00 | 2023-11-06T18:10:50 |
NONE
| null |
### Issue you'd like to raise.
How can i use Human Tool along with the websocket. As we cannot use async with tool.Can anyone please provide the solution for this.
def please_take_test():
score = 0
for i, data in enumerate(questions):
print(ask_for_info(data["question"], data["options"]))
provided_answer = str(input())
score += check_answer(provided_answer, data["answer"])
first_prompt = ChatPromptTemplate.from_template(
"""You are the assisant to greet the user on the basis of score
"""
)
info_gathering_chain = LLMChain(llm=llm, prompt=first_prompt)
ai_chat = info_gathering_chain.run(score = score)
return ai_chat
Here I need this input() should be directly taken from the user interface
### Suggestion:
_No response_
|
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| 1,816,302,776 |
I_kwDOIPDwls5sQpS4
| 8,094 |
retrieve email metadata from email stored as pdf
|
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[] | null | 2 | 2023-07-21T19:40:54 | 2023-10-27T16:04:53 | 2023-10-27T16:04:52 |
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.
### 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](https://github.com/tevslin/emailai). If there's interest I will do the linting etc to submit as a pr.
|
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PR_kwDOIPDwls5WH85T
| 8,093 |
Removing score threshold parameter of faiss _similarity_search_with_r…
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[] | null | 5 | 2023-07-21T19:33:15 | 2023-08-04T04:31:43 | 2023-08-04T04:31:43 |
CONTRIBUTOR
| null |
Removing score threshold parameter of faiss _similarity_search_with_relevance_scores as the thresholding part is implemented in similarity_search_with_relevance_scores method which calls this method.
As this method is supposed to be a private method of faiss.py this will never receive the score threshold parameter as it is popped in the super method similarity_search_with_relevance_scores.
@baskaryan @hwchase17
|
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| 1,816,275,731 |
PR_kwDOIPDwls5WH4mi
| 8,092 |
remove CVEs
|
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[] | null | 8 | 2023-07-21T19:12:38 | 2024-01-09T14:20:20 | 2023-07-21T20:32:39 |
COLLABORATOR
| null |
This PR aims to move all code with CVEs into `langchain.experimental`. Note that we are NOT yet removing from the core `langchain` package - we will give people a week to migrate here.
See MIGRATE.md for how to migrate
Zero changes to functionality
Vulnerabilities this addresses:
PALChain:
- https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5752409
- https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759265
SQLDatabaseChain
- https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759268
`load_prompt` (Python files only)
- https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5725807
|
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PR_kwDOIPDwls5WH4Hr
| 8,091 |
add kwargs support for Baseten models
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[] | null | 3 | 2023-07-21T19:10:20 | 2023-07-21T20:57:27 | 2023-07-21T20:56:27 |
CONTRIBUTOR
| null |
This bugfix PR adds kwargs support to Baseten model invocations so that e.g. the following script works properly:
```python
chatgpt_chain = LLMChain(
llm=Baseten(model="MODEL_ID"),
prompt=prompt,
verbose=False,
memory=ConversationBufferWindowMemory(k=2),
llm_kwargs={"max_length": 4096}
)
```
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PR_kwDOIPDwls5WH1qR
| 8,090 |
use top nav docs
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[] | null | 1 | 2023-07-21T18:58:52 | 2023-07-21T20:52:05 | 2023-07-21T20:52:04 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5sQR5Y
| 8,089 |
Issue: OpenSearch painless scripting scoring function could be improved
|
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[] | null | 1 | 2023-07-21T18:19:26 | 2023-10-27T16:04:59 | 2023-10-27T16:04:58 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Painless scripting shows an example scoring function here
https://opensearch.org/docs/latest/search-plugins/knn/painless-functions/#get-started-with-k-nns-painless-scripting-functions
You can see that they reference the query vector by `params.query_value`. This is better, in my opinion than simply pasting it in as we do here
https://github.com/hwchase17/langchain/blob/17c06ee45634cafb9aa8fcd06848a4213b2d58e2/libs/langchain/langchain/vectorstores/opensearch_vector_search.py#L246
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5WHpiR
| 8,088 |
update contributor guide
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[] | null | 1 | 2023-07-21T18:19:02 | 2023-07-21T19:01:06 | 2023-07-21T19:01:05 |
COLLABORATOR
| null | null |
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| 1,816,203,822 |
I_kwDOIPDwls5sQRIu
| 8,087 |
YoutubeLoader to load documents from transcripts as chunks
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[] | null | 4 | 2023-07-21T18:16:32 | 2023-10-28T16:04:30 | 2023-10-28T16:04:29 |
NONE
| null |
### Feature request
Currently, The YoutubeLoader class loads transcripts and converts all of them into a single large piece of text loosing all the information regarding when it was said in the video to be able to reference it later.
I understand we cannot have very small/ unpridictable lengths of transcripts, which is why it would be great to chunk them according to a input duration by user and retain some information regarding when its said. which get the best of both worlds by having large enough data and also retaining context
### Motivation
I was trying to make a vedio qa retriever, Then I noticed how the transcripts were being loaded, I have also noticed this issue with other transcript loaders such as VTT, then I had to make my custom function to load the transcripts in chunks of duration that made sense to me, which lead me to making this issue
### Your contribution
I already have the code for chunking the transcripts, would love to submit a PR.
|
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PR_kwDOIPDwls5WHnlf
| 8,086 |
Issue #8089 Improve painless script scoring with params.query_value.
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[] | null | 1 | 2023-07-21T18:11:33 | 2023-08-04T06:06:18 | 2023-08-04T06:06:18 |
CONTRIBUTOR
| null |
This is a minor improvement that replaces the full query_vector with the reference string `params.query_value` used in the painless scripting docs. I have tested it manually and it works on an example. This makes the query about half the size and much easier to read.
https://opensearch.org/docs/latest/search-plugins/knn/painless-functions/#get-started-with-k-nns-painless-scripting-functions
@babbldev
#8089
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| 1,816,157,145 |
PR_kwDOIPDwls5WHe2o
| 8,085 |
Issue 8081 Fix query results size bug. Other bug: pass vector_field param.
|
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[] | null | 2 | 2023-07-21T17:36:37 | 2023-08-04T05:41:12 | 2023-08-04T05:41:11 |
CONTRIBUTOR
| null |
@baskaryan
#8081
Likely the reason why the issue occurred is that OpenSearch's default k is 10, so it needs to be specified.
Here's a similar question about its cousin ElasticSearch
https://discuss.elastic.co/t/elasticsearch-returns-only-10-records-but-the-hit-is-507/136605
I tested this manually and also fixed the same issue in `_default_painless_scripting_query`. In addition, `_default_painless_scripting_query` was not passing the `vector_field` name to a sub call, so I fixed that too.
I also tested this in the aws opensearch developer tools.
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| 1,816,134,814 |
PR_kwDOIPDwls5WHaHE
| 8,084 |
Harrison/move experimental
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[] | null | 1 | 2023-07-21T17:16:23 | 2023-07-21T17:36:29 | 2023-07-21T17:36:28 |
COLLABORATOR
| null | null |
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| 1,816,098,954 |
PR_kwDOIPDwls5WHSYE
| 8,083 |
fix api ref
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[] | null | 1 | 2023-07-21T16:43:35 | 2023-07-21T19:37:22 | 2023-07-21T19:37:21 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,816,069,417 |
PR_kwDOIPDwls5WHMEW
| 8,082 |
add kwargs support to Baseten LLMs
|
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[] | null | 2 | 2023-07-21T16:18:10 | 2023-07-21T19:11:28 | 2023-07-21T19:05:12 |
CONTRIBUTOR
| null |
This bugfix PR adds kwargs support to Baseten model invocations so that e.g. the following script works properly:
```
chatgpt_chain = LLMChain(
llm=Baseten(model="MODEL_ID"),
prompt=prompt,
verbose=False,
memory=ConversationBufferWindowMemory(k=2),
llm_kwargs={"max_length": 4096}
)
``
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| 1,816,067,589 |
I_kwDOIPDwls5sPv4F
| 8,081 |
OpenSearchVectorStore's similarity search functions can't handle k>10 with script_scoring
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[] | null | 6 | 2023-07-21T16:16:32 | 2023-10-27T16:05:09 | 2023-10-27T16:05:08 |
NONE
| null |
### System Info
Langchain version 0.0.225
M1 Mac
Python 3.11.3
### Who can help?
@naveentatikonda
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import OpenSearchVectorSearch
from opensearchpy import RequestsHttpConnection
from requests_aws4auth import AWS4Auth
import os
aws_auth = AWS4Auth(
os.environ["AWS_ACCESS_KEY"],
os.environ["AWS_SECRET_KEY"],
"us-east-1",
"es"
)
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/distiluse-base-multilingual-cased-v2",
model_kwargs={'device': 'cpu'}
)
opensearch_vector_search = OpenSearchVectorSearch(
os.environ["OPENSEARCH_URL"],
"testindex",
embeddings,
http_auth=aws_auth,
connection_class=RequestsHttpConnection,
)
texts = []
for i in range(1, 41):
texts.append(f"This is document {i}")
opensearch_vector_search.add_texts(texts)
query = "This is a document"
search_result = opensearch_vector_search._raw_similarity_search_with_score(
query,
k=30,
metadata_field="*",
search_type="script_scoring"
)
print("Number of docs in response: ", len(search_result))
```
Output:
```
Number of docs in response: 10
```
When using script_scoring, the code does not pass a "size=k" option to the client query, so it returns a maximum of 10 hits by default. Therefore, the user cannot receive more than 10 results regardless of the value of k. I believe all that is necessary it to add a `size=k` parameter to line 537 in `opensearch_vector_search.py`, and then the `[:k]` in line 539 is superfluous.
### Expected behavior
The above code should produce 30 results, not 10.
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| 8,080 |
Calling the OpenAI Functions agent with an empty tool list crashes
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[] | null | 2 | 2023-07-21T15:52:04 | 2023-11-01T16:06:10 | 2023-11-01T16:06:09 |
NONE
| null |
### System Info
langchain version 0.0.238
openai version 0.27.8
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Set up your Open AI keys in your .env file then run this python script:
```py
from dotenv import load_dotenv
from langchain.agents import initialize_agent, AgentType
from langchain.chat_models import ChatOpenAI
load_dotenv()
llm = ChatOpenAI(model_name="gpt-3.5-turbo")
agent = initialize_agent(
tools=[],
llm=llm,
agent=AgentType.OPENAI_FUNCTIONS
)
agent.run("Hello World!")
```
### Expected behavior
The agent should be run successfully and just not be able to use any tools if none are provided. This does happen if the functions parameter is not passed to the openai interface when its empty. So should be an easy fix:
1. Detect if the tools list is empty
2. Only pass the function parameter to the openai interface if its not empty
The interface call in question (`langchain/chat_models/openai.py` in line 319):
```py
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return self.client.create(**kwargs) # <-- HERE, just filter out key functions if empty
```
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I_kwDOIPDwls5sPa_9
| 8,079 |
Missing Enum support in OpenAI Functions
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[] | null | 1 | 2023-07-21T15:15:39 | 2023-10-27T16:05:19 | 2023-10-27T16:05:18 |
CONTRIBUTOR
| null |
Can be fixed by carrying over openapi enum definitions to new `enum` field here:
https://github.com/hwchase17/langchain/blob/95e369b38dfc8fcb55c8d1ac435ad40e326e653d/langchain/chains/openai_functions/openapi.py#L85
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PR_kwDOIPDwls5WGs2l
| 8,078 |
Fix InvalidVectorQuery error
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[] | null | 8 | 2023-07-21T14:39:33 | 2023-07-28T04:59:49 | 2023-07-28T04:59:48 |
NONE
| null |
- Description: Fixed the InvalidVectorQuery error in several functions of the AzureSearch class in azuresearch.py. The changes were based on the code found in [this Azure cognitive-search-vector-pr sample](https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python/code/azure-search-vector-recall-python-sample.ipynb).
- Issue: This addresses an observed InvalidVectorQuery error when using AzureSearch with vectors.
- Dependencies: With this fix, the import `from azure.search.documents.models import Vector` is no longer necessary and has been removed.
- Tag maintainer: @rlancemartin, @eyurtsev
- Issue: #7841
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PR_kwDOIPDwls5WGlH1
| 8,077 |
bump 239
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[] | null | 1 | 2023-07-21T14:17:01 | 2023-07-21T14:31:15 | 2023-07-21T14:31:14 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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2. an example notebook showing its use.
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| 1,815,862,551 |
PR_kwDOIPDwls5WGfMe
| 8,076 |
Change with_history option to False for ChatGLM by default
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[] | null | 1 | 2023-07-21T13:59:20 | 2023-07-24T22:46:03 | 2023-07-24T22:46:02 |
CONTRIBUTOR
| null |
ChatGLM LLM integration will by default accumulate conversation history(with_history=True) to ChatGLM backend api, which is not expected in most cases. This PR set with_history=False by default, user should explicitly set llm.with_history=True to turn this feature on. Related PR: #8048 #7774
<!-- Thank you for contributing to LangChain!
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| 1,815,844,430 |
PR_kwDOIPDwls5WGbP5
| 8,075 |
Change JSONLoader content_key behavior
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[] | null | 4 | 2023-07-21T13:48:19 | 2023-09-29T16:26:28 | 2023-09-29T16:26:28 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
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-->
### Description
Changed the value specified for `content_key` in JSONLoader from a single key to a value based on jq schema.
### Why
For json data like the following, specify `.data[].attributes.message` for page_content and `.data[].attributes.id` or `.data[].attributes.attributes. tags`, etc., the `content_key` must also parse the json structure.
<details>
<summary>sample json data</summary>
```json
{
"data": [
{
"attributes": {
"attributes": {
"dd": {
"service": "worker",
"env": "production",
"version": "6b2c46a5883a9097aa2cb09907786b5c06ca3bd0"
},
"source": "stderr",
"service": "worker",
"name": "app.services.predict_services",
"levelname": "ERROR",
"container_id": "f9f880b243cc41f1a7d9bae5bf922d60-1262558729",
"timestamp": 1686679407827.0
},
"message": "error while processing user #19084: 'numpy.dtype[bool_]' object is not callable",
"service": "worker",
"status": "error",
"tags": [
"datadog.submission_auth:private_api_key",
"env:production"
],
"timestamp": "2023-06-14T03:03:27.827000+09:00"
},
"id": "AgAAAYi17UzTUeIczgAAAAAAAAAYAAAAAEFZaTE3VThXQUFEOF9sS2J4Z3psRmdBRAAAACQAAAAAMDE4OGI2MzYtMmZlNC00ZDEwLThjZDMtMzhkZTI0NmUyNWMz",
"type": "log"
},
{
"attributes": {
"attributes": {
"dd": {
"service": "worker",
"env": "production",
"version": "6b2c46a5883a9097aa2cb09907786b5c06ca3bd0"
},
"process": 42.0,
"messages": "error while processing user #19084: 'numpy.dtype[bool_]' object is not callable",
"levelname": "ERROR",
"container_id": "f9f880b243cc41f1a7d9bae5bf922d60-1262558729",
"timestamp": 1686679407831.0
},
"message": "{\"messages\": \"error while processing user #19084: 'numpy.dtype[bool_]' object is not callable\"}",
"service": "worker",
"status": "error",
"tags": [
"datadog.submission_auth:private_api_key",
"env:production"
],
"timestamp": "2023-06-14T03:03:27.831000+09:00"
},
"id": "AgAAAYi17UzXUeIczwAAAAAAAAAYAAAAAEFZaTE3VThXQUFEOF9sS2J4Z3psRmdBRQAAACQAAAAAMDE4OGI2MzYtMmZlNC00ZDEwLThjZDMtMzhkZTI0NmUyNWMz",
"type": "log"
}
],
"meta": {
"elapsed": 26,
"request_id": "pddv1ChY0SmttaDA0a1REeXZRM01yNkFwYnd3Ii0KHWIANr8mghGpsMIX2cOarI6t4WyTVObXx3wrAuudEgzSbtmduLtPxFVkSo0",
"status": "done"
}
}
```
</details>
<details>
<summary>sample code</summary>
```python
def metadata_func(record: dict, metadata: dict) -> dict:
print(record)
metadata["id"] = record.get("id")
metadata["tags"] = record["attributes"].get("tags")
return metadata
sample_file = "sample.json"
loader = JSONLoader(
file_path=sample_file,
jq_schema='.data[].attributes.message',
content_key="data",
metadata_func=metadata_func
)
```
</details>
### Dependencies
none
### Tag maintainer
@rlancemartin, @eyurtsev
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
|
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| 1,815,838,663 |
I_kwDOIPDwls5sO3_H
| 8,074 |
DOC: Fix issues related to Vertex AI LLMs
|
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[] | null | 0 | 2023-07-21T13:44:36 | 2023-08-18T05:56:11 | 2023-08-18T05:56:11 |
CONTRIBUTOR
| null |
### Issue with current documentation:
https://python.langchain.com/docs/modules/model_io/models/llms/integrations/google_vertex_ai_palm - a few things are already outdated.
### Idea or request for content:
_No response_
|
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| 1,815,819,807 |
PR_kwDOIPDwls5WGWBK
| 8,073 |
feat: add USearch vectorstore
|
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[] | null | 1 | 2023-07-21T13:32:10 | 2023-07-24T09:10:41 | 2023-07-24T09:10:41 |
CONTRIBUTOR
| null |
## Description
Add USearch vectorstore for LangChain
[USearch](https://github.com/unum-cloud/usearch)
## How to use
```python
from langchain.vectorstores.usearch import USearch
usearch = USearch.from_texts(texts, embeddings)
|
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| 8,072 |
Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 1 | 2023-07-21T13:24:57 | 2023-10-27T16:05:23 | 2023-10-27T16:05:22 |
NONE
| null |
### Issue you'd like to raise.
This is that the documentation has to be improved
from langchain.indexes.vectorstore import VectorstoreIndexWrapper
to from langchain.indexes.vectorstore import VectorStoreIndexWrapper
<img width="762" alt="image" src="https://github.com/hwchase17/langchain/assets/32350453/7ef0656c-69ca-4f09-bc49-fb4230eaf93a">
### Suggestion:
edit this **from langchain.indexes.vectorstore import VectorstoreIndexWrapper**
to **from langchain.indexes.vectorstore import VectorStoreIndexWrapper**
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| 8,071 |
feat: add USearch vectorstore
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[] | null | 1 | 2023-07-21T13:11:22 | 2023-07-21T13:26:07 | 2023-07-21T13:26:06 |
CONTRIBUTOR
| null |
## Description
Add USearch vectorstore for LangChain
[USearch](https://github.com/unum-cloud/usearch)
## How to use
```python
from langchain.vectorstores.usearch import USearch
usearch = USearch.from_texts(texts, embeddings)
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| 1,815,777,052 |
PR_kwDOIPDwls5WGNLx
| 8,070 |
Don't throw JSON decode error in OpenAIFunctionsAgent
|
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[] | null | 2 | 2023-07-21T13:03:10 | 2023-08-04T04:11:10 | 2023-08-04T04:11:10 |
NONE
| null |
related to https://github.com/appfolio/af_reactor/issues/1438
### Expected behavior
For a `BaseTool` the OpenAIFunctionCallingAgent reliably outputs a response of the form
```json
{"name": "<tool_name>", "arguments": {"__arg1": "<tool_input>"}}
```
### Actual behavior
For some inputs it formats the response as
```json
{"name": "<tool_name>", "arguments": "<tool_input>"}
```
which leads to a `JSONDecodeError`. The agent doesn't seem to follow the "hacky" format very reliably, even when explicitly encouraged in the system prompt.
@hinthornw
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
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Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
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See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5sOXCC
| 8,069 |
unable to implement memory with sql agent toolkit
|
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[] | null | 7 | 2023-07-21T12:12:30 | 2023-10-29T16:04:52 | 2023-10-29T16:04:51 |
NONE
| null |
### System Info
I am using langchain 0.0.238, I want to use ConversationBufferMemory with sql-agent-toolkit. Please support in this regard.
I changed prompt :
# flake8: noqa
SQL_PREFIX = """You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, else fetch all results then fetch all results donot use 'LIMIT'.
When you are using 'WHERE' clause to fetch specific data, you have to apply 'WHERE' clause in several ways like exact matchusing '=' or substring match using 'LIKE'.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question. If you did notfind the column asked by the user, then return "data for the specified column doesnot exists." as the answer.
You have access to tools for interacting with the database.
Only use the below tools. Only use the information returned by the below tools to construct your final answer.
You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.
DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.
If the question does not seem related to the database, just return "I don't know" as the answer.
This reminds you of your past memory and events:{chat_history}
Know that only the "Question" ("HumanMessage") and "Final answer" ("AIMessage") to each previous question are available inthe past memory section above.
Hence, always include enough detail in the Final answer so that you can answer subsequent questions.
If you believe any of the data computed to answer a question is likely to be useful in answering a possible follow-on question.
"""
SQL_SUFFIX = """Begin!
Question: {input}
Thought: I should look at the tables in the database to see what I can query. Then I should query the schema of the most relevant tables.
{agent_scratchpad}"""
SQL_FUNCTIONS_SUFFIX = """I should look at the tables in the database to see what I can query. Then I should query the schema of the most relevant tables."""
And changed base.py:
"""SQL agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
#from sql_agent_toolkits.sql.prompt import ( SQL_FUNCTIONS_SUFFIX,SQL_PREFIX,SQL_SUFFIX,)
from langchain.agents.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain.agents.agent_types import AgentType
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
from langchain.schema.language_model import BaseLanguageModel
from langchain.schema.messages import AIMessage, SystemMessage
from langchain.memory import ConversationBufferMemory
global memory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
def create_sql_agent(
llm: BaseLanguageModel,
toolkit: SQLDatabaseToolkit,
agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = SQL_PREFIX,
suffix: Optional[str] = SQL_SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
top_k: int = 10,
max_iterations: Optional[int] = 15,
max_execution_time: Optional[float] = None,
early_stopping_method: str = "force",
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Dict[str, Any],
) -> AgentExecutor:
"""Construct an SQL agent from an LLM and tools."""
tools = toolkit.get_tools()
#prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
#print("prefix", prefix)
if input_variables is None:
input_variables = ["dialect", "input", "agent_scratchpad"]
agent: BaseSingleActionAgent
print("input_variables", input_variables)
if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix or SQL_SUFFIX,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
# memory = ConversationBufferMemory(memory_key="chat_history")
elif agent_type == AgentType.OPENAI_FUNCTIONS:
messages = [
SystemMessage(content=prefix),
HumanMessagePromptTemplate.from_template("{input}"),
AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
input_variables = ["input", "agent_scratchpad"]
_prompt = ChatPromptTemplate(input_variables=input_variables, messages=messages)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_manager=callback_manager,
**kwargs,
)
else:
raise ValueError(f"Agent type {agent_type} not supported at the moment.")
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
callback_manager=callback_manager,
verbose=verbose,
max_iterations=max_iterations,
max_execution_time=max_execution_time,
early_stopping_method=early_stopping_method,
memory=memory,
**(agent_executor_kwargs or {}),
)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have changed Prompt.py and base.py to provide chat_history as input but I am running into errors from create_prompt method using ZeroShotAgent or from LLMChain(expecting inputs in dictionary)
Error:
input_variables ['dialect', 'input', 'agent_scratchpad']
input_variables from /opt/miniconda/lib/python3.10/site-packages/langchain/agents/mrkl/ ['dialect', 'input', 'agent_scratchpad']
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[1], line 2
1 from tools.db_llm_with_memory import *
----> 2 db_llm_with_memory("which all enzymes have urate as substrate")
File ~/notebooks/atossa-usecase/atossa_usecase/tools/db_llm_with_memory.py:42, in db_llm_with_memory(query)
35 agent = create_sql_agent(
36 llm=ChatOpenAI(temperature=0),
37 toolkit=toolkit,
38 verbose=True,
39 agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,memory=memory
40 )
41 # agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=toolkit, verbose=True, memory=memory)
---> 42 agent.run(query)
File /opt/miniconda/lib/python3.10/site-packages/langchain/chains/base.py:440, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
438 if len(args) != 1:
439 raise ValueError("`run` supports only one positional argument.")
--> 440 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
441 _output_key
442 ]
444 if kwargs and not args:
445 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
446 _output_key
447 ]
File /opt/miniconda/lib/python3.10/site-packages/langchain/chains/base.py:220, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
185 def __call__(
186 self,
187 inputs: Union[Dict[str, Any], Any],
(...)
193 include_run_info: bool = False,
194 ) -> Dict[str, Any]:
195 """Execute the chain.
196
197 Args:
(...)
218 `Chain.output_keys`.
219 """
--> 220 inputs = self.prep_inputs(inputs)
221 callback_manager = CallbackManager.configure(
222 callbacks,
223 self.callbacks,
(...)
228 self.metadata,
229 )
230 new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
File /opt/miniconda/lib/python3.10/site-packages/langchain/chains/base.py:364, in Chain.prep_inputs(self, inputs)
362 _input_keys = _input_keys.difference(self.memory.memory_variables)
363 if len(_input_keys) != 1:
--> 364 raise ValueError(
365 f"A single string input was passed in, but this chain expects "
366 f"multiple inputs ({_input_keys}). When a chain expects "
367 f"multiple inputs, please call it by passing in a dictionary, "
368 "eg `chain({'foo': 1, 'bar': 2})`"
369 )
370 inputs = {list(_input_keys)[0]: inputs}
371 if self.memory is not None:
ValueError: A single string input was passed in, but this chain expects multiple inputs ({'dialect', 'input'}). When a chain expects multiple inputs, please call it by passing in a dictionary, eg `chain({'foo': 1, 'bar': 2})`
### Expected behavior
Please provide some simple addon behaviour to use memory with sql agent
|
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| 1,815,656,226 |
I_kwDOIPDwls5sOLci
| 8,068 |
Integration test on BingSearchAPIWrapper
|
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[] | null | 2 | 2023-07-21T11:36:16 | 2023-10-27T16:05:34 | 2023-10-27T16:05:33 |
CONTRIBUTOR
| null |
### Feature request
Hello, I have a proposal on adding Integration TCs on BingSearchAPIWrapper, in utilities/bing_search, similar with GoogleSerperAPIWrapper.
As bing search provides several API endpoints like [Bing News Search API](https://www.microsoft.com/en-us/bing/apis/bing-news-search-api), I thought it would be good to add some TCs on bing search, to make baseline for further implementation on other bing apis.
For the test, "BING_SUBSCRIPTION_KEY" would be necessary.
### Motivation
As bing search provides several API endpoints like [Bing News Search API](https://www.microsoft.com/en-us/bing/apis/bing-news-search-api), I thought it would be good to add some TCs on bing search, to make baseline for further implementation on other bing apis.
### Your contribution
If you allow me, i would like to contribute on this.
|
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| 1,815,491,917 |
I_kwDOIPDwls5sNjVN
| 8,066 |
Handle simpler tool input schemas for structured tools
|
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[] | null | 10 | 2023-07-21T09:36:30 | 2023-11-05T16:05:35 | 2023-11-05T16:05:34 |
NONE
| null |
### Feature request
Recognize input schemas that do not use `Field` as structured schemas.
### Motivation
A simple schema without `Field` values such as this one will fail to be recognized as a structured schema:
```python
class RepeatTextSchema(BaseModel):
text: str
occurences: int
```
It will thus give such an error:
```
occurences
field required (type=value_error.missing)
```
I guess it would provide a greater developer experience if any schema with multiple variables would be recognized as a structured schema.
### Your contribution
None so far but happy to help if I can.
|
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I_kwDOIPDwls5sNiJ5
| 8,065 |
Async @tool functions
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[] | null | 1 | 2023-07-21T09:33:16 | 2023-10-27T16:05:38 | 2023-10-27T16:05:38 |
NONE
| null |
### Feature request
Support async functions with the `@tool` decorator (and `StructuredTool.from_function`, I guess).
### Motivation
The API provided by `@tool` is really nice, but using the decorator on an async function currently doesn't work.
### Your contribution
None so far but happy to help if I can.
|
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| 1,815,420,202 |
PR_kwDOIPDwls5WE_r4
| 8,064 |
Fix `allowed_search_types` in `VectorStoreRetriever`
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[] | null | 1 | 2023-07-21T08:50:13 | 2023-07-22T01:47:08 | 2023-07-21T15:39:37 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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I guess `allowed_search_types` is unexpectedly changed in https://github.com/hwchase17/langchain/commit/6792a3557dc396804a12ba8892b36a322434c7c6, so that we cannot specify `similarity_score_threshold` here.
```python
class VectorStoreRetriever(BaseRetriever):
...
allowed_search_types: ClassVar[Collection[str]] = (
"similarity",
"similarityatscore_threshold",
"mmr",
)
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
search_type = values["search_type"]
if search_type not in cls.allowed_search_types:
raise ValueError(...)
if search_type == "similarity_score_threshold":
... # UNREACHABLE CODE
```
VectorStores Maintainers: @rlancemartin @eyurtsev
|
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https://api.github.com/repos/langchain-ai/langchain/issues/8062
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I_kwDOIPDwls5sNPs1
| 8,062 |
Issue:in the multiprompt chain connecting sqldatabasechain
|
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[] | null | 2 | 2023-07-21T08:43:59 | 2023-10-27T16:05:43 | 2023-10-27T16:05:43 |
NONE
| null |
### Issue you'd like to raise.
def from_prompts(
cls,
llm: BaseLanguageModel,
db: SQLDatabase,
prompt_infos: List[Dict[str, str]],
default_chain: Optional[LLMChain] = None,
verbose: bool = False,
**kwargs: Any,
) -> Self:
"""Convenience constructor for instantiating from destination prompts."""
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_SQL_ROUTER_TEMPLATE.format(
destinations=destinations_str
)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(next_inputs_inner_key="query"),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
prompt = PromptTemplate(template=prompt_template,
input_variables=["input" ],
)
chain = SQLDatabaseChain.from_llm(llm, db,prompt=prompt,
output_key="text",
return_intermediate_steps=True,
verbose=True,)
destination_chains[name] = chain
_default_chain = default_chain or ConversationChain(llm=llm, output_key="text")
return cls(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=_default_chain,
**kwargs,
)
If the enter prompt is not related to database the how can we route them to the defaultchain Or LLMchain and check the logs on which prompt info is executed for that particular prompt.
### Suggestion:
_No response_
|
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| 1,815,375,116 |
I_kwDOIPDwls5sNG0M
| 8,061 |
Bug: Chroma().add_texts() return empty ids list when metadata is not None
|
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[] | null | 3 | 2023-07-21T08:17:06 | 2023-07-24T01:18:49 | 2023-07-24T01:18:49 |
NONE
| null |
### System Info
LangChain version: '0.0.238'
### Who can help?
SemanticSimilarityExampleSelector().add_example() raise "IndexError" exception due to empty list ids returned from Chroma().add_texts() when metadata is not None.
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.prompts.example_selector.semantic_similarity import SemanticSimilarityExampleSelector
from langchain.vectorstores import Chroma
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
examples_dict = [{'a':'b'}]
embedding_function = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
example_selector = SemanticSimilarityExampleSelector.from_examples(
# This is the list of examples available to select from.
examples_dict,
# This is the embedding class used to produce embeddings which are used to measure semantic similarity.
embedding_function,
# This is the VectorStore class that is used to store the embeddings and do a similarity search over.
Chroma, # type: ignore
# This is the number of examples to produce and include per prompt
k=min(3, len(examples_dict)),
)
example_selector.add_example(examples_dict[0])
### Expected behavior
ids returned from Chroma().add_texts() should be a list of ids of texts are added
|
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