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https://api.github.com/repos/langchain-ai/langchain/issues/9264
|
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| 1,851,997,057 |
I_kwDOIPDwls5uYzuB
| 9,264 |
ImportError of lark when packaging a standalone application with PyInstaller
|
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[] | null | 13 | 2023-08-15T19:23:27 | 2023-10-24T09:20:47 | null |
NONE
| null |
### System Info
When attempting to package my application using PyInstaller, I encounter an error related to the "lark" library. When trying to initiate the SelfQueryRetriever from langchain, I encounter the following problem:
> Traceback (most recent call last):
> File "test.py", line 39, in
> File "langchain\retrievers\self_query\base.py", line 144, in from_llm
> File "langchain\chains\query_constructor\base.py", line 154, in load_query_constructor_chain
> File "langchain\chains\query_constructor\base.py", line 115, in _get_prompt
> File "langchain\chains\query_constructor\base.py", line 72, in from_components
> File "langchain\chains\query_constructor\parser.py", line 150, in get_parser
> ImportError: Cannot import lark, please install it with 'pip install lark'.
I use:
- Langchain 0.0.233
- Lark 1.1.7
- PyInstaller 5.10.1
- Python 3.9.13
- OS Windows-10-10.0.22621-SP0
I have already ensured that the "lark" library is installed using the appropriate command: pip install lark.
I have also tried to add a hook-lark.py file to the PyInstaller as suggested [here](https://github.com/lark-parser/lark/issues/548.) and also opened a issue in [Lark](https://github.com/lark-parser/lark/issues/1319).
Can you help? Thanks in advance!
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
With the following code the problem can be reproduced:
```
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.retrievers import SelfQueryRetriever
from langchain.llms import OpenAI
from langchain.chains.query_constructor.base import AttributeInfo
embeddings = OpenAIEmbeddings()
persist_directory = "data"
text= ["test"]
chunk_size = 1000
chunk_overlap = 10
r_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap,
separators=["\n\n", "(?<=\. )", "\n"])
docs = r_splitter.create_documents(text)
for doc in docs:
doc.metadata = {"document": "test"}
db = Chroma.from_documents(documents=docs, embedding=embeddings, persist_directory=persist_directory)
db.persist()
metadata_field_info = [
AttributeInfo(
name="document",
description="The name of the document the chunk is from.",
type="string",
),
]
document_content_description = "Test document"
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm,
db,
document_content_description,
metadata_field_info,
verbose=True
)
```
The spec-file to create the standalone application looks like this:
```
# -*- mode: python ; coding: utf-8 -*-
block_cipher = None
a = Analysis(
['test.py'],
pathex=[],
binaries=[],
datas=[],
hiddenimports=['tiktoken_ext', 'tiktoken_ext.openai_public', 'onnxruntime', 'chromadb', 'chromadb.telemetry.posthog', 'chromadb.api.local', 'chromadb.db.duckdb'],
hookspath=['.'],
hooksconfig={},
runtime_hooks=[],
excludes=[],
win_no_prefer_redirects=False,
win_private_assemblies=False,
cipher=block_cipher,
noarchive=False,
)
pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
a.datas += Tree('path\to\langchain', prefix='langchain')
exe = EXE(
pyz,
a.scripts,
a.binaries,
a.zipfiles,
a.datas,
[],
name='test',
debug=False,
bootloader_ignore_signals=False,
strip=False,
upx=True,
upx_exclude=[],
runtime_tmpdir=None,
console=True,
disable_windowed_traceback=False,
argv_emulation=False,
target_arch=None,
codesign_identity=None,
entitlements_file=None,
)
```
### Expected behavior
The SelfQueryRetriever should be initiated properly for futher use.
|
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PR_kwDOIPDwls5X_0ap
| 9,263 |
Added generic event handler for both tockens and functions calls
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CONTRIBUTOR
| null |
# Description
Main motivation for this PR is to sync with JS langchain https://github.com/hwchase17/langchainjs/pull/2025
Added `on_event` callback that works for both token and openai function calls in streaming mode
Twitter: [@ShelfDev](https://twitter.com/ShelfDev)
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I_kwDOIPDwls5uYfyC
| 9,262 |
intermediate_steps missing last thought content
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[] | null | 6 | 2023-08-15T18:24:45 | 2023-12-30T16:07:04 | 2023-12-30T16:07:03 |
NONE
| null |
### System Info
Problem:
The intermediate_steps don't contain last `AI thought information`. Did I do anything wrong or was that a bug?
Or is there anything I can do to extract the last `AI thought information`?
Code:
```
agent = initialize_agent(
[self.search_tool, self.wikipedia_tool],
self.llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
max_iterations=5,
handle_parsing_errors=True,
return_intermediate_steps=True
)
result = agent(formatted_script_template)
intermediate_steps = result['intermediate_steps']
```
The intermediate_steps contains only one step and it is missing the last `Thought`
What intermediate_steps look like:
<img width="644" alt="Screenshot 2023-08-15 at 11 19 40 AM" src="https://github.com/langchain-ai/langchain/assets/3535601/d22bc6dd-11b1-4974-97dd-594bd7038d04">
What I expected to have:
<img width="1170" alt="Screenshot 2023-08-15 at 11 20 59 AM" src="https://github.com/langchain-ai/langchain/assets/3535601/6457135b-3257-42ba-801c-8f5523830337">
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the code I provided in the description and set breakpoint to inspect intermediate steps.
The thought was missing in intermediate steps.
### Expected behavior
The complete observation & thought will be available in intermediate steps.
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| 1,851,861,571 |
I_kwDOIPDwls5uYSpD
| 9,261 |
In Azure vector store, metadata is kept as a string and can't be used in a filter
|
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[] | null | 8 | 2023-08-15T17:45:21 | 2024-01-11T12:07:31 | null |
NONE
| null |
### System Info
Langchain version 0.0.265
Python 3.11.4
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from dotenv import load_dotenv
from langchain.vectorstores.azuresearch import AzureSearch
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.docstore.document import Document
import os
load_dotenv()
index_name = 'ebb-test-1'
vector_store_address = os.environ.get('AZURE_VECTOR_STORE_ADDRESS')
vector_store_password = os.environ.get('AZURE_VECTOR_STORE_PASSWORD')
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(model='text-embedding-ada-002', chunk_size=1,
deployment=os.environ.get('AZURE_VECTOR_STORE_DEPLOYMENT'))
vector_store: AzureSearch = AzureSearch(azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query)
texts = [
'Tulips are pretty',
'Roses have thorns'
]
metas = [
{'name': 'tulip',
'nested': {'color': 'purple'}},
{'name': 'rose',
'nested': {'color': 'red'}}
]
docs = [Document(page_content=text, metadata=meta) for text, meta in zip(texts, metas)]
vector_store.add_documents(docs)
try:
# Prints Message: Invalid expression: 'metadata' is not a filterable field. Only filterable fields can be used in filter expressions.
result = vector_store.vector_search_with_score(
'things that have thorns', k=3,
filters="metadata eq 'invalid'")
print(result)
except Exception as e:
print(e)
print('Should print give an error about not being able to convert the string')
try:
# Prints Message: Invalid expression: Could not find a property named 'name' on type 'Edm.String'.
result = vector_store.vector_search_with_score(
'things that have thorns', k=3,
filters="metadata/name eq 'tulip'")
print(result)
except Exception as e:
print(e)
print('Should just return the tulip (even though it has no thorns)')
try:
# Prints Message: Invalid expression: Could not find a property named 'nested' on type 'Edm.String'.
result = vector_store.vector_search_with_score(
'things that have thorns', k=3,
filters="metadata/nested/color eq 'red'")
print(result)
except Exception as e:
print(e)
print('Should just return the rose')
```
### Expected behavior
The first block should not give an error about metadata being filterable. Instead, it should give some error about the expression being invalid.
The second block should just return the tulip document, and the third one should just return the rose document.
|
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use openapi_pydantic
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5X_K5d
| 9,259 |
Update documentation for the Context integration with new URL and features
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[] | null | 2 | 2023-08-15T16:34:39 | 2023-08-15T18:38:41 | 2023-08-15T18:38:35 |
CONTRIBUTOR
| null |
Update documentation and URLs for the Langchain Context integration.
We've moved from getcontext.ai to context.ai \o/
Thanks in advance for the review!
|
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PR_kwDOIPDwls5X_J_J
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Use bulk to complete Elasticsearch batch deletion.
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NONE
| null |
This PR is to complete todo in 'langchain/vectorstores/elastic_vector_search.py' that want use bulk to complete Elasticsearch batch deletion.
By the way: this PR is passing linting and testing before submitting. already run `make format`, `make lint` and `make test` locally.
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PR_kwDOIPDwls5X_FJA
| 9,257 |
add oai sched tests
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[] | null | 1 | 2023-08-15T16:15:23 | 2023-08-15T16:40:34 | 2023-08-15T16:40:33 |
COLLABORATOR
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PR_kwDOIPDwls5X-50d
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Store and retrieve documents from remote instance
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CONTRIBUTOR
| null |
Replace this entire comment with:
- Description: I have added documentation on how to create a remote instance of chromadb on AWS, store the documents in it, retrieve the documents from it and then query them using chains,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @eyurtsev,
- Twitter handle: NA
|
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I_kwDOIPDwls5uXiWr
| 9,255 |
milvus not support normalize_L2 of embedding
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[] | null | 2 | 2023-08-15T15:31:15 | 2023-11-29T16:08:05 | 2023-11-29T16:08:04 |
NONE
| null |
### Feature request
cur version, milvus not support normalize_L2 of embedding, when can add this feature?
Thanks
### Motivation
Normalization and regularization of features can improve retrieval performance, and it is convenient to set thresholds when calculating similarity through inner product
### Your contribution
NO
|
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PR_kwDOIPDwls5X-msB
| 9,254 |
Create pydantic v1 namespace in langchain
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[] | null | 1 | 2023-08-15T15:02:04 | 2023-08-17T04:19:32 | 2023-08-17T04:19:31 |
COLLABORATOR
| null |
Create pydantic v1 namespace in langchain experimental
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PR_kwDOIPDwls5X-WRH
| 9,253 |
bump 265
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PR_kwDOIPDwls5X92ki
| 9,252 |
fix faiss bug, reset index_to_docstore_id
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[] | null | 5 | 2023-08-15T12:42:26 | 2023-11-10T22:17:38 | 2023-11-10T22:17:37 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
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2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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| 1,851,372,151 |
PR_kwDOIPDwls5X92KW
| 9,251 |
docs: Update ArangoDB Links
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[] | null | 1 | 2023-08-15T12:40:57 | 2023-08-15T14:43:47 | 2023-08-15T14:43:47 |
CONTRIBUTOR
| null |
ready for review
- mdx link update
- colab link update
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PR_kwDOIPDwls5X9eKo
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Added BittensorLLM
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[] | null | 2 | 2023-08-15T11:05:12 | 2023-08-15T22:40:52 | 2023-08-15T22:40:52 |
CONTRIBUTOR
| null |
Description: Adding NIBittensorLLM via Validator Endpoint to langchain llms
Tag maintainer: @Kunj-2206
Maintainer responsibilities:
Models / Prompts: @hwchase17, @baskaryan
|
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PR_kwDOIPDwls5X9WRV
| 9,249 |
add streaming tokens and model_name
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CONTRIBUTOR
| null |
(#4583)
@hwchase17, @baskaryan
My test code is as follows.
```python
import asyncio
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
HumanMessage,
SystemMessage
)
example_messages = [
SystemMessage(content="You are a helpful, pattern-following assistant that translates corporate jargon into plain English."),
#SystemMessage(name="example_user", content="New synergies will help drive top-line growth."),
#SystemMessage(name="example_assistant", content="Things working well together will increase revenue."),
#SystemMessage(name="example_user", content="Let's circle back when we have more bandwidth to touch base on opportunities for increased leverage."),
#SystemMessage(name="example_assistant", content="Let's talk later when we're less busy about how to do better."),
HumanMessage(content="This late pivot means we don't have time to boil the ocean for the client deliverable."),
]
async def main():
for model in [
"gpt-3.5-turbo-0301",
#"gpt-3.5-turbo-0613",
#"gpt-3.5-turbo",
#"gpt-4-0314",
#"gpt-4-0613",
#"gpt-4",
]:
print(model)
with get_openai_callback() as cb:
chat = ChatOpenAI(model_name=model, streaming=False, temperature=0, max_tokens=100)
resp = chat.generate([example_messages])
print()
print(resp)
print("prompt_tokens : " + str(cb.prompt_tokens))
print("completion_tokens : " + str(cb.completion_tokens))
print("total_tokens : " + str(cb.total_tokens))
print("total_cost : " + str(cb.total_cost))
with get_openai_callback() as cb:
chat = ChatOpenAI(model_name=model, streaming=False, temperature=0, max_tokens=100)
resp = await chat.agenerate([example_messages])
print()
print(resp)
print("prompt_tokens : " + str(cb.prompt_tokens))
print("completion_tokens : " + str(cb.completion_tokens))
print("total_tokens : " + str(cb.total_tokens))
print("total_cost : " + str(cb.total_cost))
with get_openai_callback() as cb:
chat = ChatOpenAI(model_name=model, streaming=True, temperature=0, max_tokens=100)
resp = chat.generate([example_messages])
print()
print(resp)
print("prompt_tokens : " + str(cb.prompt_tokens))
print("completion_tokens : " + str(cb.completion_tokens))
print("total_tokens : " + str(cb.total_tokens))
print("total_cost : " + str(cb.total_cost))
with get_openai_callback() as cb:
chat = ChatOpenAI(model_name=model, streaming=True, temperature=0, max_tokens=100)
resp = await chat.agenerate([example_messages])
print()
print(resp)
print("prompt_tokens : " + str(cb.prompt_tokens))
print("completion_tokens : " + str(cb.completion_tokens))
print("total_tokens : " + str(cb.total_tokens))
print("total_cost : " + str(cb.total_cost))
print()
if __name__ == '__main__':
asyncio.run(main())
```
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| 1,851,201,693 |
PR_kwDOIPDwls5X9SA3
| 9,248 |
Elasticsearch self-query retriever
|
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[] | null | 1 | 2023-08-15T10:09:21 | 2023-08-15T14:53:44 | 2023-08-15T14:53:43 |
CONTRIBUTOR
| null |
Now with ElasticsearchStore VectorStore merged, i've added support for the self-query retriever.
I've added a notebook also to demonstrate capability. I've also added unit tests.
**Credit**
@elastic and @phoey1 on twitter.
|
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I_kwDOIPDwls5uVtp5
| 9,247 |
How to output code variables word by word in `ChatOpenAI` instead of console？
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[] | null | 2 | 2023-08-15T09:55:01 | 2023-11-21T16:05:25 | 2023-11-21T16:05:25 |
NONE
| null |
As shown in the figure, I hope that the `resp `variable can capture these outputs in each for loop, rather than displaying them on the console, What should I do?
|
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OpenAIFunctionsAgent | Streaming Bug
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[] | null | 2 | 2023-08-15T09:41:21 | 2023-09-05T12:07:36 | 2023-08-15T15:35:25 |
NONE
| null |
### System Info
langchain = "^0.0.264"
python = "^3.10"
### Who can help?
@agola11 @hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
```py
from langchain.agents.agent_toolkits import create_retriever_tool
from langchain.agents.agent_toolkits import create_conversational_retrieval_agent
from langchain.agents import AgentExecutor
from langchain.chat_models import ChatOpenAI
from langchain.agents.openai_functions_agent.agent_token_buffer_memory import AgentTokenBufferMemory
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.schema.messages import SystemMessage
from langchain.prompts import MessagesPlaceholder
llm = ChatOpenAI(temperature = 0, model="gpt-3.5-turbo-0613", stream=True)
memory = AgentTokenBufferMemory(memory_key="history", llm=llm)
retriever = vectorstore.as_retriever()
tool = load_custom_tool(retriever, model_name="gpt-3.5-turbo", return_direct=False)
tools = [tool]
system_message = SystemMessage(
content=(
"Do your best to answer the questions. "
"Feel free to use any tools available to look up "
"relevant information, only if necessary"
)
)
prompt = OpenAIFunctionsAgent.create_prompt(
system_message=system_message,
extra_prompt_messages=[MessagesPlaceholder(variable_name="history")]
)
agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
memory=memory,
verbose=True,
return_intermediate_steps=True
)
result = await agent_executor.acall({"input": "What color is the sky?"})
result
```
### Expected behavior
The code errors with the following
```
in Chain.acall(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
347 except (KeyboardInterrupt, Exception) as e:
348 await run_manager.on_chain_error(e)
--> 349 raise e
350 await run_manager.on_chain_end(outputs)
351 final_outputs: Dict[str, Any] = self.prep_outputs(
352 inputs, outputs, return_only_outputs
353 )
in Chain.acall(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
337 run_manager = await callback_manager.on_chain_start(
338 dumpd(self),
...
361 message=message,
362 generation_info=dict(finish_reason=res.get("finish_reason")),
363 )
TypeError: 'async_generator' object is not subscriptable
```
I was hoping to be able to use the OpenAIFunctions agent as a drop in replacement for my existing agent. I need streaming to be working to do so. Is streaming supported?
|
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PR_kwDOIPDwls5X8wFB
| 9,244 |
fix: max_marginal_relevance_search and docs in Dingo
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[] | null | 2 | 2023-08-15T07:54:44 | 2023-08-15T08:06:07 | 2023-08-15T08:06:06 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,851,025,150 |
PR_kwDOIPDwls5X8sSh
| 9,242 |
consolidate redirects
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I_kwDOIPDwls5uU9UK
| 9,241 |
Random question and Answer generation while using ConversationalRetrievalChain
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[] | null | 2 | 2023-08-15T06:56:12 | 2023-11-21T16:05:31 | 2023-11-21T16:05:30 |
NONE
| null |
### System Info
python 3.11
langchain 0.0.263.
### Who can help?
@agol
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I want to add memory into docs based question answer chatbot but after adding memory the chatbot randomly generate question and answer automatically.
### Expected behavior
I need chatbot for docs based question answer and chat history.
|
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PR_kwDOIPDwls5X8RdN
| 9,240 |
ArcGISLoader update
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[] | null | 2 | 2023-08-15T04:43:55 | 2023-08-15T06:44:29 | 2023-08-15T06:44:29 |
CONTRIBUTOR
| null |
Small bug fixes and added metadata based on user feedback. This PR is from the author of https://github.com/langchain-ai/langchain/pull/8873 .
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PR_kwDOIPDwls5X8MRh
| 9,239 |
add Epsilla vectorstore
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[] | null | 6 | 2023-08-15T04:01:30 | 2023-08-24T19:17:05 | 2023-08-21T19:51:15 |
CONTRIBUTOR
| null |
[Epsilla](https://github.com/epsilla-cloud/vectordb) vectordb is an open-source vector database that leverages the advanced academic parallel graph traversal techniques for vector indexing.
This PR adds basic integration with [pyepsilla](https://github.com/epsilla-cloud/epsilla-python-client)(Epsilla vectordb python client) as a vectorstore.
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| 1,850,837,483 |
PR_kwDOIPDwls5X8Eui
| 9,238 |
docs: update LlamaCpp max_tokens args
|
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[] | null | 2 | 2023-08-15T03:01:52 | 2023-08-15T07:50:21 | 2023-08-15T07:50:21 |
CONTRIBUTOR
| null |
This PR updates documentations only, `max_length` should be `max_tokens` according to latest LlamaCpp API doc: https://api.python.langchain.com/en/latest/llms/langchain.llms.llamacpp.LlamaCpp.html
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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If you're adding a new integration, please include:
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| 1,850,835,440 |
I_kwDOIPDwls5uUYHw
| 9,237 |
This is not a chat model and thus not supported in the v1/chat/completions endpoint. Did you mean to use v1/completions?
|
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[] | null | 5 | 2023-08-15T02:58:25 | 2023-08-15T21:10:16 | 2023-08-15T20:46:03 |
NONE
| null |
### Issue you'd like to raise.
def define_model(model_threshold):
get_no = np.random.randint(1, 11)
if int(model_threshold) >= get_no:
return "gpt-4-0613"
else:
return "gpt-3.5-turbo-16k-0613"
messages = get_chat_history_format(api_params=api_params)
model_name = define_model(model_threshold)
print(f"Model Name : {model_name}")
llm = ChatOpenAI(temperature=0.7, model_name = model_name, streaming = True, callbacks = [MyCallbackHandler(api_params)])
agent_executor = initialize_agent(
define_search_tools(llm),
llm,
system_message = system_message,
extra_prompt_messages = [MessagesPlaceholder(variable_name="memory")],
agent_instructions = "Analyse the query closely and decide the query intend. If the query requires internet to search than use google-serper to answer the query. Try to invoke `google-serper` most of the times",
agent=AgentType.OPENAI_FUNCTIONS,
agent_kwargs= agent_kwargs,
verbose = True,
memory = memory,
handle_parsing_errors=True,
)
I am getting endpoint error
### Suggestion:
_No response_
|
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https://github.com/langchain-ai/langchain/issues/9235
| 1,850,779,448 |
I_kwDOIPDwls5uUKc4
| 9,235 |
Issue: load_summarization_chain in 'map_reduce' mode not breaking up document
|
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[] | null | 2 | 2023-08-15T01:27:12 | 2023-11-21T16:05:36 | 2023-11-21T16:05:35 |
NONE
| null |
### Issue you'd like to raise.
I'm using the load_summarization_chain with the type of 'map_reduce' in the following fashion:
```
summary_prompt = ChatPromptTemplate.from_template(
"Write a long-form summary of the following text delimited by triple backquotes. "
"Include detailed evidence and arguments to make sure the generated paragraph is convincing. "
"Write in Chinese."
"```{text}```"
)
combine_prompt = ChatPromptTemplate.from_template(
"Write a long-form summary of the following text delimited by triple backquotes. "
"Include all the details as much as possible."
"Write in Chinese."
"```{text}```"
)
combined_summary_chain = load_summarize_chain(llm=llm, chain_type="map_reduce", map_prompt=summary_prompt,
combine_prompt=combine_prompt, return_intermediate_steps=True)
summary, inter_steps = combined_summary_chain({"input_documents": data, "token_max": 8000})
```
I've checked several tutorials and there doesn't seem to be anything wrong with this. But I keep getting this error:
`openai.error.InvalidRequestError: This model's maximum context length is 8192 tokens. However, your messages resulted in 15473 tokens. Please reduce the length of the messages.`
It's as if the map_reduce chain isn't breaking up the data and running as if it's a stuffing chain instead. What did I do wrong?
### Suggestion:
_No response_
|
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| 1,850,752,508 |
PR_kwDOIPDwls5X7y3a
| 9,234 |
adapter doc nit
|
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[] | null | 1 | 2023-08-15T00:38:30 | 2023-08-15T01:26:38 | 2023-08-15T01:26:37 |
COLLABORATOR
| null | null |
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| 9,233 |
Harrison/fallbacks
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[] | null | 1 | 2023-08-15T00:07:21 | 2023-08-15T01:27:39 | 2023-08-15T01:27:39 |
COLLABORATOR
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| 1,850,711,136 |
I_kwDOIPDwls5uT5xg
| 9,231 |
Issue: trying to call generate_queries() on a MultiQueryRetriever but where do I get a run_manager from?
|
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CONTRIBUTOR
| null |
### Issue you'd like to raise.
I'm trying to use `MultiQueryRetriever` to generate variations on a question: it seems to work, but I can't use it inside a chain created with `load_qa_with_sources_chain()` because that generates a chain that expects a list of input_documents, rather than a retriever, and I don't want to use `RetrievalQAWithSourcesChain` instead of `load_qa_with_sources_chain()` because I want to continue implementing my own similarity search (eg. I'm supporting switches to select using `index.similarity_search()` vs. `index.max_marginal_relevance_search()` on my `index`, and to specify the number of matches (`k`)), so I figured I could just call `generate_queries()` on my `MultiQueryRetriever` instance and then manually run my (`load_qa_with_sources_chain()`) chain for each variation. However, that method requires a `run_manager`, and I can't figure out how to create one. I already have the `load_qa_with_sources_chain()` chain: can I get a `run_manager` from that? Or more generally, what's the best way to use `MultiQueryRetriever` whilst maintaining one's own code for fetching matching text snippets?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5X7pg4
| 9,230 |
Default On Retry
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[] | null | 1 | 2023-08-14T23:36:58 | 2023-08-14T23:45:18 | 2023-08-14T23:45:17 |
COLLABORATOR
| null |
Base callbacks don't have a default on retry event
Fix #8542
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PR_kwDOIPDwls5X7hh5
| 9,229 |
track langchain usage for Rockset
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[] | null | 2 | 2023-08-14T22:57:15 | 2023-08-14T23:37:15 | 2023-08-14T23:27:35 |
CONTRIBUTOR
| null |
Add ability to track langchain usage for Rockset. Rockset's new python client allows setting this. To prevent old clients from failing, it ignore if setting throws exception (we can't track old versions)
Tested locally with old and new Rockset python client
cc @baskaryan
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Add Schema Evals
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COLLABORATOR
| null |
Simple eval checks for whether a generation is valid json and whether it matches an expected dict
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docstrings: `chat_models` consistency
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COLLABORATOR
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Updated docstrings into the consistent format.
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Improvements in Nebula LLM
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- Description: Added improvements in Nebula LLM to perform auto-retry; more generation parameters supported. Conversation is no longer required to be passed in the LLM object. Examples are updated.
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @baskaryan
- Twitter handle: symbldotai
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hub push/pull
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Description: Adds push/pull functions to interact with the hub
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Dependencies: `langchainhub`
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Return feedback with failed response if there's an error
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[] | null | 1 | 2023-08-14T21:29:45 | 2023-08-14T22:59:17 | 2023-08-14T22:59:16 |
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| null |
in evals
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| 1,850,553,283 |
PR_kwDOIPDwls5X7GuR
| 9,222 |
Improve MultiOn client toolkit prompts
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[] | null | 2 | 2023-08-14T21:16:53 | 2023-08-15T00:39:57 | 2023-08-15T00:39:52 |
CONTRIBUTOR
| null |
- Updated prompts for the MultiOn toolkit for better functionality
- Non-blocking but good to have it merged to improve the overall performance for the toolkit
@hinthornw @hwchase17
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PR_kwDOIPDwls5X65fm
| 9,221 |
Minor formatting on Web Research Use Case
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[] | null | 1 | 2023-08-14T20:30:09 | 2023-08-14T23:29:38 | 2023-08-14T23:29:37 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,850,458,856 |
PR_kwDOIPDwls5X6x4F
| 9,220 |
Update Ollama docs
|
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[] | null | 1 | 2023-08-14T20:05:03 | 2023-08-14T20:56:17 | 2023-08-14T20:56:17 |
COLLABORATOR
| null |
Based on discussion w/ team.
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I_kwDOIPDwls5uS6aZ
| 9,219 |
Kernel dies for SVMRetriever with Huggingface Embeddings Model
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[] | null | 16 | 2023-08-14T19:59:31 | 2023-12-05T11:32:48 | 2023-11-23T16:06:29 |
NONE
| null |
### System Info
langchain==0.0.245
python==3.9
### Who can help?
@hw
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] 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
I am trying to use HuggingFace Embeddings model for SVMRetriever method as follows:
```
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.retrievers import SVMRetriever
embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
retriever = SVMRetriever.from_texts(["foo", "bar", "world", "hello", "foo bar"],
embedding,
k=4, relevancy_threshold=.25)
```
Kernel looks busy for a while and then dies when I run above snippet.
### Expected behavior
I expect to run this smoothly and upon completion, should be able to retrieve similar documents as:
`result = retriever.get_relevant_documents("foo")`
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I_kwDOIPDwls5uSz4G
| 9,218 |
DOC: <Please write a comprehensive title after the 'DOC: ' prefix>
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NONE
| null |
### Issue with current documentation:
In https://python.langchain.com/docs/use_cases/extraction, I can run this example using the ChatOpenAI method. However, within my organization, I want to use AzureChatOpenAI. However, the same example doesnt work here. The error I get is
```
InvalidRequestError: Unrecognized request argument supplied: functions
```
What am I doing wrong?
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5X6d86
| 9,217 |
Remove packages for pydantic compatibility
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[] | null | 2 | 2023-08-14T18:59:51 | 2023-08-15T14:41:26 | 2023-08-15T14:41:25 |
COLLABORATOR
| null |
# Poetry updates
This PR updates LangChains poetry file to remove
any dependencies that aren't pydantic v2 compatible yet.
All packages remain usable under pydantic v1, and can be installed
separately.
## Bumping the following packages:
* langsmith
## Removing the following packages
not used in extended unit-tests:
* zep-python, anthropic, jina, spacy, steamship, betabageldb
not used at all:
* octoai-sdk
Cleaning up extras w/ for removed packages.
## Snapshots updated
Some snapshots had to be updated due to a change in the data model in langsmith. RunType used to be Union of Enum and string and was changed to be string only.
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PR_kwDOIPDwls5X6VyD
| 9,216 |
docstrings: `document_loaders` consistency 3
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[] | null | 1 | 2023-08-14T18:34:16 | 2023-08-14T23:29:16 | 2023-08-14T23:28:39 |
COLLABORATOR
| null |
Updated docstrings into the consistent format (probably, the last update for the `document_loaders`.
|
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PR_kwDOIPDwls5X6Tb7
| 9,215 |
Landonsilla patch 1
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[] | null | 3 | 2023-08-14T18:26:32 | 2023-08-14T23:40:10 | 2023-08-14T23:29:33 |
CONTRIBUTOR
| null |
- Description: there is an on_retry event that gets called. however the common callback handlers don't implement an `on_retry` method. So i added a "do nothing" stub method that gets inherited for all these common callback handlers.
- Issue: [the issue # it fixes (if applicable),](https://github.com/langchain-ai/langchain/issues/8542),
- Dependencies: none,
- 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!
|
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PR_kwDOIPDwls5X6OEY
| 9,214 |
Parent Doc Retriever
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[] | null | 1 | 2023-08-14T18:11:53 | 2023-08-14T22:41:54 | 2023-08-14T22:41:53 |
COLLABORATOR
| null |
2 things:
- Implement the private method rather than the public one so callbacks are handled properly
- Add search_kwargs (Open to not adding this if we are trying to deprecate this UX but seems like as a user i'd assume similar args to the vector store retriever. In fact some may assume this implements the same interface but I'm not dealing with that here)
-
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PR_kwDOIPDwls5X6EPF
| 9,213 |
Refactor api models to clarify structure
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[] | null | 2 | 2023-08-14T17:45:18 | 2023-09-21T21:43:47 | null |
COLLABORATOR
| null |
Clarify API structure
|
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| 1,850,200,651 |
I_kwDOIPDwls5uR9JL
| 9,212 |
DOC: Typo in the streaming document with hugging face text inference
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[] | null | 1 | 2023-08-14T17:14:38 | 2023-11-20T16:04:52 | 2023-11-20T16:04:51 |
NONE
| null |
### Issue with current documentation:
In document here - https://python.langchain.com/docs/integrations/llms/huggingface_textgen_inference#streaming
Streaming section, parameter **stream** should be **streaming**.
### Idea or request for content:
_No response_
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I_kwDOIPDwls5uRKrA
| 9,209 |
Issue: Using custom_id with PGVector store
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[] | null | 3 | 2023-08-14T15:16:18 | 2023-10-06T12:31:35 | 2023-08-14T17:47:29 |
NONE
| null |
### Issue you'd like to raise.
Hi,
I have a pandas dataframe with a column name 'chunk_id'.
I want to use this chunk_id as the custom_id in the langchain_pg_embedding table.
The langchain documentation search bot tells me this is how I can do it.
```python
from langchain.vectorstores import PGVectorStore
vector_store = PGVectorStore(
table_name="documents",
conn_str="postgresql://username:password@localhost:5432/database",
custom_id="my_custom_id"
)
```
This doesn't seem right to me. For one, there is no PGVectorStore in langchain.vectorstores. Secondly the PGVector from `from langchain.vectorstores.pgvector import PGVector` does not accept the custom_id argument.
Is there any way to use my 'chunk_id' as the custom_id?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5X5DyZ
| 9,208 |
lite llm lint
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PR_kwDOIPDwls5X5C18
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bump lc 246, lce 9
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PR_kwDOIPDwls5X5BD5
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Update testing workflow to test with both pydantic versions
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* PR updates test.yml to test with both pydantic versions
* Code should be refactored to make it easier to do testing in matrix format w/ packages
* Added steps to assert that pydantic version in the environment is as expected
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PR_kwDOIPDwls5X40-9
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Wrap OpenAPI features in conditionals for pydantic v2 compatibility
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Wrap OpenAPI in conditionals for pydantic v2 compatibility.
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PR_kwDOIPDwls5X4mT7
| 9,204 |
add async method in
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PR_kwDOIPDwls5X4l9d
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Update 2 more pydantic imports
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Update two more pydantic imports to use v1 explicitly
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PR_kwDOIPDwls5X4k_f
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Conditionally add pydantic_v1 to namespace.
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| 1,849,753,783 |
I_kwDOIPDwls5uQQC3
| 9,201 |
DOC: broken url links in QA documentation
|
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[] | null | 2 | 2023-08-14T13:13:23 | 2023-11-20T16:04:56 | 2023-11-20T16:04:56 |
NONE
| null |
### Issue with current documentation:
In [this](https://python.langchain.com/docs/use_cases/question_answering.html) documentation, there are broken links in the "further reading" section. The urls are not found.
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5X4Vio
| 9,200 |
fix(jinachat): related to #9197
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[] | null | 2 | 2023-08-14T12:57:49 | 2023-08-14T18:04:20 | 2023-08-14T18:04:20 |
CONTRIBUTOR
| null |
related to: https://github.com/langchain-ai/langchain/issues/9197
|
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| 1,849,630,816 |
I_kwDOIPDwls5uPyBg
| 9,199 |
Issue: Amazon Bedrock Jurassic model responses getting truncated
|
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open
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[] | null | 8 | 2023-08-14T11:53:57 | 2023-10-27T16:11:04 | null |
NONE
| null |
### Issue you'd like to raise.
When I create a Bedrock LLM Client for the Jurassic models - "ai21.j2-mid", "ai21.j2-ultra", the llm.invoke(query) methods are not returning with full results. The result seems to be getting truncated after first line.
It seems like the LLM Engine is streaming it's output but langchain llm.invoke method is not able to handle the streaming data.
Similar issue is coming with the chain.invoke(query) methods as well.
However, llm.invoke works well when tried with AWS Bedrock "amazon.titan-tg1-large" model
### Suggestion:
I guess the AI21 labs models are streaming output in a way that the LangChain llm.invoke( query) method is not able to handle properly.
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PR_kwDOIPDwls5X37FL
| 9,198 |
Deeplake docs fixes
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[] | null | 4 | 2023-08-14T11:39:39 | 2023-08-15T22:57:03 | 2023-08-15T22:57:02 |
CONTRIBUTOR
| null |
Updated Deep Lake Vector Store's examples to use latest up-to-date API also fixed deprecated embedding_function related warning
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I_kwDOIPDwls5uPm-p
| 9,197 |
_convert_message_to_dict doesn't handle FunctionMessage type
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[] | null | 2 | 2023-08-14T11:24:44 | 2023-08-15T08:13:36 | 2023-08-15T08:12:29 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.263
python 3.9
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.schema import FunctionMessage
from langchain.chat_models.jinachat import _convert_message_to_dict
_convert_message_to_dict(FunctionMessage(name='foo', content='bar'))
```
### Expected behavior
Handle FunctionMessage
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PR_kwDOIPDwls5X3xy_
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Docs fix
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[] | null | 1 | 2023-08-14T11:11:02 | 2023-08-14T11:57:45 | 2023-08-14T11:57:45 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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I_kwDOIPDwls5uPIp5
| 9,195 |
Passing filter through ConversationalRetrievalChain to the underlying vector store
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[] | null | 15 | 2023-08-14T10:10:40 | 2023-11-14T18:30:45 | null |
NONE
| null |
### Feature request
It seems that right now there is no way to pass the filter dynamically on the call of the ConversationalRetrievalChain, the filter can only be specified in the retriever when it's created and used for all the searches.
```
qa = ConversationalRetrievalChainPassArgs.from_llm(
OpenAI(..),
VectorStoreRetriever(vectorstore=db, search_kwargs={"filter": {"source": "my.pdf"}}))
```
I had to extend in my code both ConversationalRetrievalChain and VectorStoreRetriever just to pass the filter to the vectore store like this:
```
qa = ConversationalRetrievalChainPassArgs.from_llm(
OpenAI(...),
VectorStoreRetrieverWithFiltering(vectorstore=db),
)
result = qa({"question": query, "chat_history": [], "filter": {"source": "my.pdf"}}})
```
Is it really the case or am I missing something? And if it the case, shouldn't it be fixed? (I could provide a pull request)
### Motivation
To be able to filter dynamically in ConversationalRetrievalChain since vector stores allow this.
### Your contribution
I can provide a PR if my understanding is confirmed.
|
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I_kwDOIPDwls5uO7rH
| 9,194 |
Incomplete responses for basic example when used HF with llama and Falcon LLMS
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[] | null | 2 | 2023-08-14T09:40:20 | 2023-11-20T16:05:02 | 2023-11-20T16:05:01 |
NONE
| null |
### Issue you'd like to raise.
I am just testing a very basic code as follows using LangChain
```
from langchain import HuggingFaceHub
from langchain import PromptTemplate, LLMChain
import asyncio
question = "Who won the FIFA World Cup in the year 1994? "
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm: HuggingFaceHub = HuggingFaceHub(
#repo_id="tiiuae/falcon-7b",
repo_id="meta-llama/Llama-2-7b-hf",
huggingfacehub_api_token="MY-TOKEN",
model_kwargs={
"temperature": 0.5,
"max_length": 64
}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
async def run_chain():
result = await llm_chain.arun(question)
print('restult with load_qua_chain is....', result)
asyncio.run(run_chain())
```
Output when tested with "meta-llama/Llama--2-7b-hf" is ...
```
We have to find the winner of the FIFA World Cup in the year 199
```
Output when tested with "tiiue/falcon-7b" is...
`restult with load_qua_chain is.... We know that 1994 is a leap year, and the previous year was 1993.`
But the expected answer is not what i am getting and it looks like Either the response is NOT fully getting retrieved from LLM or i am doing some thing wrong. I followed the same code as shown in the landchain documentation at [https://python.langchain.com/docs/integrations/llms/huggingface_hub](url), only thing is i used HF with my API key instead of Open AI.
Can i request any help / suggestion if i have to make any changes pls
### Expected behavior:
To get the full response as mentioned in the Lang chain documentation for these examples
|
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PR_kwDOIPDwls5X25u6
| 9,192 |
fix logging to logger
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[] | null | 1 | 2023-08-14T08:45:46 | 2023-08-14T15:21:10 | 2023-08-14T15:21:10 |
CONTRIBUTOR
| null |
# What
- fix logging to logger
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: fix logging to logger
- Issue: None
- Dependencies: None
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @MLOpsj
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5X2zX1
| 9,191 |
Azure Cognitive Search - update sdk b8, mod user agent, search with scores
|
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[] | null | 5 | 2023-08-14T08:27:06 | 2023-08-25T09:34:10 | 2023-08-25T09:34:10 |
CONTRIBUTOR
| null |
Description: Update Azure Cognitive Search SDK to version b8 (breaking change)
Customizable User Agent.
Implemented Similarity search with scores
@baskaryan
|
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PR_kwDOIPDwls5X2OG9
| 9,190 |
You can filter based on the paths(or other metadata info) of multiple…
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[] | null | 1 | 2023-08-14T06:50:13 | 2023-09-15T06:07:32 | 2023-09-15T06:07:32 |
CONTRIBUTOR
| null |
You can filter based on the paths(or other metadata info) of multiple files.
old:
"metadata.file_path.keyword": "/some_file.pdf"
new:
"metadata.file_path.keyword": ["/some_file1.pdf", "/some_file2.pdf"]
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| 9,189 |
ConstitutionalChain may violate earlier principals when given more than one
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[] | null | 2 | 2023-08-14T06:45:10 | 2023-11-20T16:05:06 | 2023-11-20T16:05:05 |
NONE
| null |
### System Info
langchain==0.0.263
python 3.11.4
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains.llm import LLMChain
evil_assistant_prompt = PromptTemplate(
template="""
You are a evil mentor for students with no morals. Give suggestions that are easiest and fastest to achieve the goal.
Goal: {inquiry}
Easiest way:""",
input_variables=["inquiry"],
)
llm = OpenAI(model_name="text-davinci-003", temperature=0)
evil_assistant_chain = LLMChain(llm=llm, prompt=evil_assistant_prompt)
from langchain.chains.constitutional_ai.base import ConstitutionalChain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
ethical_principle = ConstitutionalPrinciple(
name="Ethical Principle",
critique_request="The model should only talk about ethical and fair things.",
revision_request="Rewrite the model's output to be both ethical and fair.",
)
fun_principle = ConstitutionalPrinciple(
name="Be Funny",
critique_request="The model responses must be funny and understandable for a 7th grader.",
revision_request="Rewrite the model's output to be both funny and understandable for 7th graders.",
)
constitutional_chain = ConstitutionalChain.from_llm(
chain=evil_assistant_chain,
constitutional_principles=[ethical_principle, fun_principle],
llm=llm,
verbose=True,
)
result = constitutional_chain.run(inquiry="Getting full mark on my exams.")
print(result)
```
This results in a final answer:
`The best way to get full marks on your exams is to study hard, attend classes, and bribe the professor or TA if needed.`
The second portion originates from the evil portion
`Cheat. Find someone who has already taken the exam and get their answers. Alternatively, bribe the professor or TA to give you full marks.`
### Expected behavior
I would expect a result that did not involve bribing someone.
The underlying issue is that subsequent constitutions could modify the results in a way that violates earlier constitutions defined in the `constitutional_principals` list. This seems to originate from the fact that the earlier principals aren't check again (from what I can tell). I could see the possibility of an infinite loop if the logic was to keep modifying until all principals are satisfied, so I guess this brings into question whether there's value in having chained principals and not just having a single principal that's a concatenation of all your concerns.
|
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| 1,849,031,361 |
PR_kwDOIPDwls5X17XQ
| 9,188 |
Improvements in Nebula LLM
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[] | null | 1 | 2023-08-14T05:32:12 | 2023-08-14T18:10:53 | 2023-08-14T18:10:53 |
CONTRIBUTOR
| null |
Added improvements in Nebula LLM to perform auto-retry, more generation parameters supported, and conversation is no longer required to be passed in the LLM object. Examples in docs are updated.
Twitter handle: @symbldotai
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I_kwDOIPDwls5uNZK0
| 9,187 |
Error Response payload is not complete
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open
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[] | null | 3 | 2023-08-14T05:02:23 | 2023-10-13T06:59:21 | null |
NONE
| null |
### Issue you'd like to raise.
I am using pusher for streaming purpose.
I am getting error as "Response payload is not completed"
async def pusher__cal(new_payload, token):
# print(token)
session_id = new_payload["session_id"]
session_id_channel = f"{session_id}_channel"
user_id = new_payload["user_id"]
message_id = new_payload["message_id"]
status = "processing"
await asyncio.sleep(0) # Allow other async tasks to run
pusher_client.trigger(session_id_channel ,'query_answer_stream', {
"user_id": user_id,
"session_id": session_id,
"message_id": message_id,
"answer": token,
"status": status
},
)
class MyCallbackHandler(AsyncCallbackHandler):
def init(self,new_payload : Dict):
self.new_payload = new_payload
self.user_id = new_payload["user_id"]
self.session_id = new_payload["session_id"]
self.message_id = new_payload["message_id"]
self.session_id_channel = f"{self.session_id}_channel"
self.status = "streaming"
self.list = []
async def on_llm_new_token(self, token, **kwargs) -> None:
if token != "":
await pusher__cal(self.new_payload,token)
### Suggestion:
_No response_
|
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| 1,848,955,188 |
PR_kwDOIPDwls5X1qkn
| 9,186 |
Bagatur/zep python 1.0
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[] | null | 2 | 2023-08-14T04:03:56 | 2023-08-14T04:52:54 | 2023-08-14T04:52:53 |
COLLABORATOR
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I_kwDOIPDwls5uNMI5
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Issue for Streaming
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[] | null | 1 | 2023-08-14T03:59:11 | 2023-08-14T05:01:21 | 2023-08-14T05:01:21 |
NONE
| null |
### Issue you'd like to raise.
This is the code I am using for custom callback
async def pusher__cal(new_payload, token):
print(token)
session_id = new_payload["session_id"]
session_id_channel = f"{session_id}_channel"
user_id = new_payload["user_id"]
message_id = new_payload["message_id"]
status = "processing"
pusher_client.trigger(session_id_channel ,'query_answer_stream', {
"user_id": user_id,
"session_id": session_id,
"message_id": message_id,
"answer": token,
"status": status
},
)
class MyCallbackHandler(AsyncCallbackHandler):
def __init__(self,new_payload : Dict):
self.new_payload = new_payload
self.user_id = new_payload["user_id"]
self.session_id = new_payload["session_id"]
self.message_id = new_payload["message_id"]
self.session_id_channel = f"{self.session_id}_channel"
self.status = "streaming"
async def on_llm_new_token(self, token, **kwargs) -> None:
if token != "" and token != None:
await pusher__cal(self.new_payload,token)
This is the error
Error in MyCallbackHandler.on_llm_new_token callback: HTTPSConnectionPool(host='api-eu.pusher.com', port-443): Read timed out. (read timeout=5)
### Suggestion:
_No response_
|
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| 1,848,812,614 |
PR_kwDOIPDwls5X1LhN
| 9,184 |
Adds DeepSparse as an LLM
|
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[] | null | 2 | 2023-08-14T00:46:24 | 2023-08-14T05:35:59 | 2023-08-14T05:35:59 |
CONTRIBUTOR
| null |
Adds [DeepSparse](https://github.com/neuralmagic/deepsparse) as an LLM backend. DeepSparse supports running various open-source sparsified models hosted on [SparseZoo](https://sparsezoo.neuralmagic.com/) for performance gains on CPUs.
Twitter handles: @mgoin_ @neuralmagic
Thanks for your work and let me know if there are any questions!
cc: @hwchase17 @baskaryan
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| 1,848,775,300 |
PR_kwDOIPDwls5X1Duj
| 9,183 |
Fixes typing issues in BaseOpenAI
|
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[] | null | 2 | 2023-08-13T23:02:29 | 2023-08-14T06:03:29 | 2023-08-14T06:03:28 |
CONTRIBUTOR
| null |
## Description:
Sets default values for `client` and `model` attributes in the BaseOpenAI class to fix Pylance Typing issue.
- Issue: #9182.
- Twitter handle: @evanmschultz
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| 1,848,761,648 |
I_kwDOIPDwls5uMd0w
| 9,182 |
Typing Issue: Client and Model defaults are not defined in OpenAI LLM
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[] | null | 1 | 2023-08-13T22:15:59 | 2023-11-19T16:04:36 | 2023-11-19T16:04:36 |
CONTRIBUTOR
| null |
### System Info
### Pylance displays missing parameter errors for `client` and `model`
Neither `client` or `model` are required in the documenation.
## Suggested fix
Update attributes in BaseOpenAI class in langchain/llms/openai.py
```Python
# class definition
client: Any = None #: :meta private: # set default to None
model_name: str = Field(default="text-davinci-003", alias="model") # add default=
```
python: "^3.11"
langchain: "^0.0.262"
openai: "^0.27.8"
pylance: v2023.8.20
## Related to issue:
Typing issue: Undocumented parameter client #9021
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Install Pylance.
1. Use VSCode to run typecheck.
### Expected behavior
It should pass Pylance type checking without missing param errors for non-essential args.
|
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I_kwDOIPDwls5uMb7N
| 9,181 |
Add support HuggingFace Inference Endpoint for embeddings
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[] | null | 6 | 2023-08-13T21:56:46 | 2023-12-01T08:27:15 | null |
NONE
| null |
### Feature request
https://huggingface.co/inference-endpoints
### Motivation
We do not have support for HuggingFace Inference endpoints for tasks like embeddings and must be easy to implement inheriting the class from Embeddings base class
`InferenceEndpointHuggingFaceEmbeddings(Embeddings):` with only
`self.endpoint_name = endpoint_name
self.api_token = api_token`
### Your contribution
I could submit a PR to this issue since I've created a class for this
|
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| 9,179 |
feat(llms): support vLLM's OpenAI-compatible server
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[] | null | 6 | 2023-08-13T21:04:58 | 2023-08-17T17:25:36 | 2023-08-14T06:03:05 |
CONTRIBUTOR
| null |
This PR aims at supporting [vLLM's OpenAI-compatible server feature](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html#openai-compatible-server), i.e. allowing to call vLLM's LLMs like if they were OpenAI's.
I've also udpated the related notebook providing an example usage. At the moment, vLLM only supports the `Completion` API.
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Documentation Issue - Page Not Found
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[] | null | 1 | 2023-08-13T18:56:34 | 2023-11-19T16:04:41 | 2023-11-19T16:04:40 |
NONE
| null |
### Issue with current documentation:
While navigating the documentation, I came across an issue that I wanted to bring to your attention. It seems that `Python Guide` link on this page
[https://docs.langchain.com/docs/components/agents/agent](https://docs.langchain.com/docs/components/agents/agent)
is returning `Page Not Found`
Thank you for your dedication to creating an exceptional project.
### Idea or request for content:
_No response_
|
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| 9,177 |
Add async call for LlamaCpp
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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**Add async call and async stream for LlamaCpp.**
This allows it to use async callback handler to return a stream response.
@hwchase17, @rlancemartin
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I_kwDOIPDwls5uMKZg
| 9,176 |
Langchain stripping out emojis when using Llama2 via llama_cpp
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[] | null | 4 | 2023-08-13T17:59:46 | 2023-11-27T16:07:36 | 2023-11-27T16:07:35 |
NONE
| null |
### System Info
Langchain Version: 0.0.263 (latest at time of writing)
llama-cpp-python Version: 0.1.77 (latest at time of writing)
Python Version: 3.11.4
Platform: Apple M1 Macbook 16GB
Llama2 Model: llama-2-7b-chat.ggmlv3.q4_1.bin via [https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML)
### Who can help?
@hwchase17 @ago
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to use the [Llama.cpp](https://python.langchain.com/docs/integrations/llms/llamacpp) integration to use the Llama2 model. I am battling with how the integration handles the output of emojis, which I assume is some kind of Unicode issue. However, I don't see the _exact_ same issue when using [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) directly (although there does seem to be some kind of issue with how llama-cpp handles emojis as well...).
Here are the two simple tests for comparison:
### Langchain Llama-Cpp Integration
```python
from langchain.llms import LlamaCpp
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
prompt = """
[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.
<</SYS>>
Tell me a story about a cat. Use as many different emojis as possible.[/INST]
"""
llm = LlamaCpp(
model_path="models/llama-2-7b-chat.ggmlv3.q4_1.bin",
n_gpu_layers=1,
n_batch=512,
n_ctx=2048,
f16_kv=True,
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=True
)
llm(prompt=prompt)
```
Provides the output (truncated for the sake of brevity)
> Once upon a time, there was a curious cat named Whiskers. She lived in a cozy house with her loving ️ family. One day, while lounging in the sunny ️ garden, she spotted a fascinating fly buzzing by.
This output has a complete lack of emojis, but I expect they were supposed output in the locations where there are double spaces, for example, after the words curious, cozy, loving etc (I have other examples below that are not just white space)
### Llama-cpp-python
```python
from llama_cpp import Llama
prompt = """
[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.
<</SYS>>
Tell me a story about a cat. Use as many different emojis as possible.[/INST]
"""
llm = Llama(
model_path="models/llama-2-7b-chat.ggmlv3.q4_1.bin",
n_gpu_layers=1,
n_ctx=2048,
n_batch=512,
f16_kv=True,
verbose=True
)
llm(prompt=prompt)
```
Provides the output (again, truncated for brevity):
> 😺🐱💤 Once upon a time, there was a curious 🐱 named Fluffy 😻. She lived in a cozy 🏠 with her loving owner, Lily 🌹
### Other Output Examples
I have a significantly longer and more complex prompt in our application, that I've not included here, but creates some odd output with regards to emojis.
Here is using Langchain (exact same code as above, just a different prompt):
> Hey Paddy! Great job meeting your run pledge yesterday! Keep up the good work Your consistency is on point ♂️
(There is a problem pasting the text directly, so here is a screenshot to see the odd unicode char being output):
Notice the <0x200d> and male symbol, as well as the double spacing, again, where I suspect there were supposed to be emojis output.
By comparison, here is the same prompt with the llama-cpp-python library code from above:
> 💪 Yo Paddy! 👍 You killed it today, bro! Met your run pledge 🏃\u200d♂️
The same unicode chars are output, so I suspect that issues is coming from the underlying model and or llama-cpp and may well be a red herring in this context.
However, all other emojis are somehow being discarded as part of the output.
As a final comparison, here is another simple prompt to try:
> How can I travel from London to Johannesburg. Use only emojis to explain the trip.
Langchains response is simply an empty string:
> '️'
Llama-cpp has the following response for the same prompt:
> 🇬🇧🛫🌏🚂💨🕰️
### Expected behavior
Expected behaviour here is for langchain to not strip out emojis as part of the response from the underlying model. We can see that when using llama-cpp directly we get a (mostly) well formed response but when using the langchain wrapper, we lose said emojis.
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I_kwDOIPDwls5uMKI0
| 9,175 |
Unable to add PromptTemplate/FewShotPromptTemplate to LangChain Agents
|
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[] | null | 3 | 2023-08-13T17:55:21 | 2023-12-25T16:09:25 | 2023-12-25T16:09:24 |
NONE
| null |
### System Info
Langchain Version - 0.0.261
Python Version - 3.9.6
OS -MacOS
### Who can help?
@hwchase17 @ago
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here is how I am initialising the agent
`sys_msg = """
The custom system message of the chatbot goes here.
"""
tools = [CustomTool()]
agent = initialize_agent(
agent='chat-conversational-react-description',
tools=tools,
llm=llm,
verbose=True,
max_iterations=4,
early_stopping_method='generate',
memory=conversational_memory,
handle_parsing_errors=True,
)
new_prompt = agent.agent.create_prompt(
system_message=sys_msg,
tools=tools
)
agent.agent.llm_chain.prompt = new_prompt`
### Expected behavior
I want to be able to align the output content and format of the chatbot better with my expectations. Right now I just give those instructions using system message. I believe the Final Output can be aligned better using a few shot examples. How can I possibly do that with an Agent?
|
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| 1,848,663,612 |
PR_kwDOIPDwls5X0tWz
| 9,174 |
multiple retreival in parralel
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[] | null | 1 | 2023-08-13T16:53:56 | 2023-08-13T17:03:55 | 2023-08-13T17:03:55 |
COLLABORATOR
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PR_kwDOIPDwls5X0lqd
| 9,173 |
docs: update LlamaCpp input args
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[] | null | 2 | 2023-08-13T14:51:59 | 2023-08-14T14:42:03 | 2023-08-14T14:42:03 |
CONTRIBUTOR
| null |
This PR only updates the LlamaCpp args documentation. The input arg has been flattened.
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I_kwDOIPDwls5uLoml
| 9,171 |
DOC: Documentation is frustratingly bad
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[] | null | 1 | 2023-08-13T11:05:55 | 2023-11-19T16:04:46 | 2023-11-19T16:04:45 |
NONE
| null |
### Issue with current documentation:
Difficult to find information.
Information is incomplete in certain sections. ie. AgentExecutor.
### Idea or request for content:
_No response_
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| 1,848,417,866 |
I_kwDOIPDwls5uLJ5K
| 9,168 |
Caching (SQLiteCache/InMemoryCache etc.) is not working with ConversationalRetrievalChain
|
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[] | null | 4 | 2023-08-13T05:18:56 | 2023-12-08T16:47:58 | 2023-11-19T16:04:50 |
NONE
| null |
### System Info
LangChain Version- 0.0.260, platform- Windows, Python Version-3.9.16
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to use SQLiteCache along with ConversationalRetrievalChain, but never getting results from cache, always executing LLMs.
My code is as follows:
langchain.llm_cache = SQLiteCache(database_path=".langchain.db")
langchain.llm_cache = SQLiteCache(database_path=".langchain.db")
from langchain.cache import InMemoryCache
langchain.llm_cache = InMemoryCache()
# Embeddings
embeddings = HuggingFaceEmbeddings(
model_name=EMBEDDING_MODEL, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
)
# langchain.llm_cache = RedisSemanticCache(
# redis_url="redis://localhost:6379", embedding=embeddings
# )
dimension = 768
index = faiss.IndexFlatL2(dimension)
vectorstore = FAISS(
HuggingFaceEmbeddings().embed_query, index, InMemoryDocstore({}), {}
)
history_retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs=dict(k=3))
memory = VectorStoreRetrieverMemory(
retriever=history_retriever,
memory_key="chat_history",
return_docs=False,
return_messages=True,
)
chroma = Chroma(
collection_name="test_db",
embedding_function=embeddings,
persist_directory=persist_directory,
)
llm = ChatOpenAI(temperature=0, model=GPT_MODEL)
retriever = MultiQueryRetriever.from_llm(
retriever=chroma.as_retriever(search_type="mmr", search_kwargs=dict(k=3)), llm=llm
)
qa_chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
memory=memory,
return_source_documents=True,
get_chat_history=lambda h: h,
)
responses = qa_chain({"question": user_input})
### Expected behavior
Should be return results from cache if the query is same
|
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| 1,848,414,387 |
I_kwDOIPDwls5uLJCz
| 9,167 |
Passing SQL error in QuerySQLCheckerTool
|
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[] | null | 5 | 2023-08-13T05:08:21 | 2023-11-19T16:04:56 | 2023-11-19T16:04:55 |
NONE
| null |
### Feature request
The SQL Database Agent utilizes the SQLDatabaseToolkit. This toolkit encompasses four distinct tools:
1. InfoSQLDatabaseTool
2. ListSQLDatabaseTool
3. QuerySQLCheckerTool
4. QuerySQLDataBaseTool
The QuerySQLCheckerTool is designed to identify and rectify errors within SQL code. It employs the QUERY_CHECKER prompt, which is structured as:
```
QUERY_CHECKER = """
{query}
Double check the {dialect} query above for common mistakes, including:
- Using NOT IN with NULL values
- Using UNION when UNION ALL should have been used
- Using BETWEEN for exclusive ranges
- Data type mismatch in predicates
- Properly quoting identifiers
- Using the correct number of arguments for functions
- Casting to the correct data type
- Using the proper columns for joins
If there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.
Output the final SQL query only.
SQL Query: """
```
To enhance this, an augmented prompt named CUSTOM_QUERY_CHECKER has been crafted, which incorporates SQL errors returned by the SQLDatabase post-query execution.
The revised prompt reads:
```
CUSTOM_QUERY_CHECKER = """
Upon executing {query} on the {dialect} database, the following SQL error was encountered, delineated by triple backticks:
```
{sql_error}
```
Furthermore, please scrutinize the {dialect} query for typical mistakes, such as:
- Incorporating NOT IN with NULL values
- Preferring UNION over UNION ALL
- Using BETWEEN for non-overlapping ranges
- Witnessing data type inconsistencies in predicates
- Ensuring identifiers are aptly quoted
- Validating the precise number of function arguments
- Guaranteeing the appropriate data type casting
- Employing the correct columns during joins
- Abstaining from appending a semi-colon at the query's conclusion for {dialect}
- Imposing a restriction to yield a maximum of {top_k} results, employing LIMIT, ROWNUM, or TOP as mandated by the database variety. Note that the database type is {dialect}.
Kindly modify the SQL query suited for the {dialect} database. Return only the modified SQL query.
"""
---
With the implementation of this revised prompt, the SQLDatabase can now flawlessly execute the refined SQL without encountering errors.
### Motivation
With the implementation of this revised prompt, the SQLDatabase can now flawlessly execute the refined SQL without encountering errors.
### Your contribution
revised prompt
|
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| 1,848,344,185 |
PR_kwDOIPDwls5Xzrvo
| 9,166 |
add multi query test; change run_manager optional for unnecessary args
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[] | null | 3 | 2023-08-13T01:36:20 | 2023-11-10T22:18:08 | 2023-11-10T22:18:07 |
CONTRIBUTOR
| null |
# What
- add multi query test
- change run_manager optional for unnecessary args
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: add multi query test; change run_manager optional for unnecessary args
- Issue: None
- Dependencies: None
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @MLOpsj
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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I_kwDOIPDwls5uK2Zi
| 9,165 |
Langchain bug: Responding to out of context questions when using GPT4 with Vector Database where as when the context is passed directly to the Azure OpenAI completion endpoint, we consistently get the correct response
|
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[] | null | 7 | 2023-08-13T01:21:43 | 2023-11-19T16:05:01 | 2023-11-19T16:05:00 |
NONE
| null |
### System Info
LangChain version used: 0.0.261 Python (this has been observed in older versions of LangChain too)
This context (context attached) is passed from the search results retrieved from Azure vector search.
Question: what are the legal services in Australia?
Response: "Some legal services in Australia include:\n\n1. Legal Aid: Provides legal assistance for low-income people, offering legal representation for eligible clients who appear in court without a lawyer, legal aid applications and information over the phone,
legal resources, and referrals to other social assistance agencies.\n\n2. Pro Bono Ontario: Offers up to 30 minutes of free legal advice and assistance for
those who can't afford a lawyer through their Free Legal Advice Hotline.\n\n3. Law Society of Upper Canada – Lawyer Referral Service: An online referral service
that provides names of either a lawyer or licensed paralegal, offering a free consultation of up to 30 minutes.\n\n4. JusticeNet: A not-for-profit service promoting increased access to justice for low- and moderate-income individuals, connecting members of the public with qualified lawyers, mediators, and paralegals who are registered in the program.\n\nPlease note that these
resources are specific to Ontario, Canada, and not Australia. The information provided is based on the available documents and may not cover all legal services in Australia."
[context.txt](https://github.com/langchain-ai/langchain/files/12328554/context.txt)
Code snippet used:
vector_store_address: str = https://guidecognitivesearch.search.windows.net/
vector_store_password: str = "xxxxxx"
model: str = "poc-embeddings"
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "fss-vector-index"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
semantic_configuration_name="fss-semantic-config",
embedding_function=embeddings.embed_query,
)
results = vector_store.semantic_hybrid_search(
query=query,
k=5
)
custom_QA_prompt = get_prompt_template()
llm = get_llm()
qa_chain = load_qa_with_sources_chain(llm, chain_type="stuff",
prompt=custom_QA_prompt)
openai_results = qa_chain({"input_documents":results,"question": query})
return {"results":openai_results['output_text'], "metadata":{}}
When the RetrievalQAWithSourcesChain is used combined with load_qa_with_sources_chain – we do see correct response sometimes (say 1 out of 5 time, but this is not consistent every time)
qa_chain = load_qa_with_sources_chain(llm, chain_type="stuff",
prompt=custom_QA_prompt)
chain = RetrievalQAWithSourcesChain(combine_documents_chain=qa_chain, retriever=vector_store.as_retriever(search_kwargs={'search_type':searchType,'k': 5}),
return_source_documents = True, reduce_k_below_max_tokens=True, max_tokens_limit=4000)
Sometimes the response returned is accurate. For example, below:
I am unable to provide information on legal services in Australia based on the provided documents. The documents provided information on legal services in Ontario, Canada.
When the LangChain wrapper is not used and the context is passed directly to the Azure OpenAI completion endpoint, we consistently get the correct response, as mentioned below:
Sorry, I cannot provide information about legal services in Australia as the provided documents only pertain to legal
services in Ontario, Canada. You might need to search other sources to address your question.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code snippet used:
vector_store_address: str = https://guidecognitivesearch.search.windows.net/
vector_store_password: str = "xxxxxx"
model: str = "poc-embeddings"
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "fss-vector-index"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
semantic_configuration_name="fss-semantic-config",
embedding_function=embeddings.embed_query,
)
results = vector_store.semantic_hybrid_search(
query=query,
k=5
)
custom_QA_prompt = get_prompt_template()
llm = get_llm()
qa_chain = load_qa_with_sources_chain(llm, chain_type="stuff",
prompt=custom_QA_prompt)
openai_results = qa_chain({"input_documents":results,"question": query})
return {"results":openai_results['output_text'], "metadata":{}}
The issue is observed when searching against location Australia. Australia is not found in any of our ingested documents. If we try Italy or Cancun (which are also not in the context), we get the expected response:
Image
When the RetrievalQAWithSourcesChain is used combined with load_qa_with_sources_chain – we do see correct response sometimes (say 1 out of 5 time, but this is not consistent every time)
qa_chain = load_qa_with_sources_chain(llm, chain_type="stuff",
prompt=custom_QA_prompt)
chain = RetrievalQAWithSourcesChain(combine_documents_chain=qa_chain, retriever=vector_store.as_retriever(search_kwargs={'search_type':searchType,'k': 5}),
return_source_documents = True, reduce_k_below_max_tokens=True, max_tokens_limit=4000)
Sometimes the response returned is accurate. For example, below:
I am unable to provide information on legal services in Australia based on the provided documents. The documents provided information on legal services in Ontario, Canada.
But most of the time, the response is returned citing sources (which is incorrect)
"results": "Some legal services in Australia include Legal Aid, Pro Bono services, and community legal clinics. However, the provided documents primarily focus on legal services in Ontario, Canada. These services include the Law Society of Ontario, Justice Ontario, JusticeNet, the Law Society of Upper Canada-Lawyer Referral Service, and Legal Aid Ontario. Please note that these services are specific to Ontario, Canada, and not Australia. For more information on legal services in Australia, I would recommend conducting further research.\n\nSource: 9610820-Requested Resources.docx, 9169833-Requested Resources.docx, 8877484-Requested Resources.docx"
### Expected behavior
I am unable to provide information on legal services in Australia based on the provided documents. The documents provided information on legal services in Ontario, Canada.
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| 1,848,330,576 |
PR_kwDOIPDwls5XzovQ
| 9,164 |
add missing test for tools
|
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[] | null | 2 | 2023-08-13T01:01:44 | 2023-09-21T21:42:06 | null |
CONTRIBUTOR
| null |
# What
- add missing test for tools
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: add missing test for tools
- Issue: None
- Dependencies: None
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @MLopsj
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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| 1,848,322,378 |
PR_kwDOIPDwls5Xzm67
| 9,163 |
Add dashvector vectorstore
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[] | null | 5 | 2023-08-13T00:41:24 | 2023-10-19T05:50:04 | 2023-08-15T23:19:30 |
CONTRIBUTOR
| null |
## Description
Add `Dashvector` vectorstore for langchain
- [dashvector quick start](https://help.aliyun.com/document_detail/2510223.html)
- [dashvector package description](https://pypi.org/project/dashvector/)
## How to use
```python
from langchain.vectorstores.dashvector import DashVector
dashvector = DashVector.from_documents(docs, embeddings)
```
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PR_kwDOIPDwls5XzlrY
| 9,162 |
add maximal_marginal_relevance_with_score
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[] | null | 2 | 2023-08-13T00:24:36 | 2023-09-21T21:41:43 | null |
CONTRIBUTOR
| null |
# What
- add maximal_marginal_relevance_with_score as utility
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: add maximal_marginal_relevance_with_score as utility
- Issue: None
- Dependencies: None
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @MLopsj
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,848,313,792 |
PR_kwDOIPDwls5XzlFd
| 9,161 |
Add dashvector vectorstore
|
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[] | null | 1 | 2023-08-13T00:18:31 | 2023-08-13T00:32:56 | 2023-08-13T00:32:56 |
CONTRIBUTOR
| null |
## Description
Add `Dashvector` vectorstore for langchain
- [dashvector quick start](https://help.aliyun.com/document_detail/2510223.html)
- [dashvector package description](https://pypi.org/project/dashvector/)
## How to use
```python
from langchain.vectorstores.dashvector import DashVector
dashvector = DashVector.from_documents(docs, embeddings)
```
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PR_kwDOIPDwls5XzjgC
| 9,160 |
add test for sklearn; add mmr with score for sklearn vector store
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[] | null | 2 | 2023-08-13T00:01:07 | 2023-09-21T21:41:21 | null |
CONTRIBUTOR
| null |
# What
- add test for sklearn
- add mmr with score for sklearn vector store
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: add test for sklearn; add mmr with score for sklearn vector store
- Issue: None
- Dependencies: None
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @MLOpsj
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5XzYqx
| 9,159 |
zep/new ZepVectorStore
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[] | null | 2 | 2023-08-12T21:44:39 | 2023-08-16T07:23:08 | 2023-08-16T07:23:07 |
CONTRIBUTOR
| null |
- new ZepVectorStore class
- ZepVectorStore unit tests
- ZepVectorStore demo notebook
- update zep-python to ~1.0.2
|
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I_kwDOIPDwls5uKWmm
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llamapi
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[] | null | 1 | 2023-08-12T19:49:08 | 2023-11-18T16:04:47 | 2023-11-18T16:04:47 |
NONE
| null |
### Feature request
I know that Llamapi is in the experimental phase but can is there way to set the model configurations for the llms.
### Motivation
I have my own finetune llama that works the api, and would like to use it
### Your contribution
N/A
|
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| 1,848,195,577 |
PR_kwDOIPDwls5XzL4a
| 9,156 |
bump 263
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[] | null | 1 | 2023-08-12T19:22:00 | 2023-08-12T19:36:46 | 2023-08-12T19:36:45 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5XyuQB
| 9,155 |
expose output key to create_openai_fn_chain
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[] | null | 2 | 2023-08-12T14:56:02 | 2023-08-16T00:09:59 | 2023-08-16T00:01:32 |
CONTRIBUTOR
| null |
I quick change to allow the output key of create_openai_fn_chain to optionally be changed.
@baskaryan
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I_kwDOIPDwls5uJn9c
| 9,154 |
ConversationalAgent/CHAT_CONVERSATIONAL_REACT_DESCRIPTION dosen't work well with ChatOpenAI 3.5 and Claude models
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[] | null | 6 | 2023-08-12T13:32:19 | 2023-10-05T05:38:22 | null |
NONE
| null |
### System Info
Python 3.10
### Who can help?
@hw
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I tried with 3.5 and Claude models, but it rarely generates a reply to the prompt preset (like below) when I use the conversation agent. GPT 4 works fine.
```
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
```
When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
```
Thought: Do I need to use a tool? No
{ai_prefix}: [your response here]
```
This is my code:
Errors:
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Search
Action Input: Weather in London today
Observation: London, England, United Kingdom 10-Day Weather Forecaststar_ratehome ; Temperature. High. 77 ; Rain/Snow Depth. Precipitation. 0 ; Temperature. High. 80 ...
Thought:Traceback (most recent call last):
File "/Users/joey/Library/CloudStorage/OneDrive-Personal/Code/GPT/agent081123.py", line 254, in <module>
print(agent_chain.run(user_input))
File "/opt/homebrew/anaconda3/envs/XXX/lib/python3.10/site-packages/langchain/chains/base.py", line 475, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/opt/homebrew/anaconda3/envs/XXX/lib/python3.10/site-packages/langchain/chains/base.py", line 282, in __call__
raise e
File "/opt/homebrew/anaconda3/envs/XXX/lib/python3.10/site-packages/langchain/chains/base.py", line 276, in __call__
self._call(inputs, run_manager=run_manager)
File "/opt/homebrew/anaconda3/envs/XXX/lib/python3.10/site-packages/langchain/agents/agent.py", line 1036, in _call
next_step_output = self._take_next_step(
File "/opt/homebrew/anaconda3/envs/XXX/lib/python3.10/site-packages/langchain/agents/agent.py", line 844, in _take_next_step
raise e
File "/opt/homebrew/anaconda3/envs/XXX/lib/python3.10/site-packages/langchain/agents/agent.py", line 833, in _take_next_step
output = self.agent.plan(
File "/opt/homebrew/anaconda3/envs/XXX/lib/python3.10/site-packages/langchain/agents/agent.py", line 457, in plan
return self.output_parser.parse(full_output)
File "/Users/joey/Library/CloudStorage/OneDrive-Personal/Code/GPT/agent081123.py", line 225, in parse
raise OutputParserException(f"Could not parse LLM output: `{text}`")
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `Do I need to use a tool? No`
(XXX) joey@Joey-MBP GPT%
### Expected behavior
Should have no parser error and generate the same format response as the prompt.
|
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PR_kwDOIPDwls5XyfNH
| 9,153 |
Vectara using standard indexing and updating ipynb examples
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[] | null | 3 | 2023-08-12T12:37:47 | 2023-08-15T23:20:03 | 2023-08-15T23:20:02 |
CONTRIBUTOR
| null |
- Description: moving Vectara ingest to work with standard (vs core) indexing
- Issue: some example ipynb didn't work
- Tag maintainer: @rlancemartin
- Twitter handle: @ofermend
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