issue_owner_repo listlengths 2 2 | issue_body stringlengths 0 261k ⌀ | issue_title stringlengths 1 925 | issue_comments_url stringlengths 56 81 | issue_comments_count int64 0 2.5k | issue_created_at stringlengths 20 20 | issue_updated_at stringlengths 20 20 | issue_html_url stringlengths 37 62 | issue_github_id int64 387k 2.46B | issue_number int64 1 127k |
|---|---|---|---|---|---|---|---|---|---|
[
"langchain-ai",
"langchain"
] | ### Feature request
A VectorSearch enabled SQLChain that is capable of doing `JOIN`, `WHERE` filters and vector search at the same time.
It should be agnostic to any SQL database backend that supports common SQL and vector search, with customizable distance function composer.
### Motivation
Hello from [MyScale](https://myscale.com) AI team! 😊👋
We have been working on features to fill up the gap among SQL, vector search and LLM applications. Some inspiring works like self-query retrievers for VectorStores (for example [Weaviate](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/weaviate_self_query.html) and [others](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query.html)) really turn those vector search databases into a powerful knowledge base! 🚀🚀
We are thinking if we can merge all in one, like SQL and vector search and LLMChains, making this SQL vector database memory as the only source of your data. Here are some benefits we can think of for now, maybe you have more 👀:
1. With ALL data you have: since you store all your pasta in the database, you don't need to worry about the foreign keys or links between names from other data source.
2. Flexible data structure: Even if you have changed your schema, for example added a table, the LLM will know how to `JOIN` those tables and use those as filters.
3. SQL compatibility: We found that vector databases that supports SQL in the marketplace have similar interfaces, which means you can change your backend with no pain, just change the name of the distance function in your DB solution and you are ready to go!
We would like to consider PGVector and MyScale for now, but if you want more, just comment below and we will push hard to ship it! 🏃🏃
### Your contribution
A PR contains the VectorSQLChain | Feature Proposal: VectorSearch enabled SQLChain? | https://api.github.com/repos/langchain-ai/langchain/issues/5122/comments | 4 | 2023-05-23T08:32:09Z | 2023-10-10T09:37:42Z | https://github.com/langchain-ai/langchain/issues/5122 | 1,721,520,077 | 5,122 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I have been trying to use SQL Database Chain with open-source LLM but have not succeeded.
`from langchain import HuggingFaceHub`
`repo_id = "google/flan-t5-xl"`
`db = SQLDatabase.from_uri(db_url)`
`llm = HuggingFaceHub(repo_id=repo_id)`
`db_chain = SQLDatabaseSequentialChain.from_llm(llm, db, verbose=True)`
Please help. Refer to any examples available online.
I have already tried gpt4all and llama.cpp
| How to use SQL Database chain with an opensource LLM? | https://api.github.com/repos/langchain-ai/langchain/issues/5121/comments | 5 | 2023-05-23T07:16:07Z | 2024-02-12T17:10:23Z | https://github.com/langchain-ai/langchain/issues/5121 | 1,721,388,198 | 5,121 |
[
"langchain-ai",
"langchain"
] | ### System Info
- platform
```
$ cat /etc/os-release
NAME="Ubuntu"
VERSION="20.04.6 LTS (Focal Fossa)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 20.04.6 LTS"
VERSION_ID="20.04"
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
VERSION_CODENAME=focal
UBUNTU_CODENAME=focal
```
- python
```
$ python -V
Python 3.9.7
# installed with
asdf install python 3.9.7
```
- problematic dependency (updated after [this comment](https://github.com/hwchase17/langchain/issues/5113#issuecomment-1558493486))
```
# this was updated today.
typing_extensions==4.6.0
```
- dependencies
```
langchain==0.0.177
openapi-schema-pydantic==1.2.4
pydantic==1.10.7
```
<details>
<summary>all the dependencies</summary>
```
$ pip install langchain
Collecting langchain
Using cached langchain-0.0.177-py3-none-any.whl (877 kB)
Collecting PyYAML>=5.4.1
Using cached PyYAML-6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (661 kB)
Collecting openapi-schema-pydantic<2.0,>=1.2
Using cached openapi_schema_pydantic-1.2.4-py3-none-any.whl (90 kB)
Collecting requests<3,>=2
Using cached requests-2.31.0-py3-none-any.whl (62 kB)
Collecting SQLAlchemy<3,>=1.4
Using cached SQLAlchemy-2.0.15-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB)
Collecting aiohttp<4.0.0,>=3.8.3
Using cached aiohttp-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)
Collecting dataclasses-json<0.6.0,>=0.5.7
Using cached dataclasses_json-0.5.7-py3-none-any.whl (25 kB)
Collecting async-timeout<5.0.0,>=4.0.0
Using cached async_timeout-4.0.2-py3-none-any.whl (5.8 kB)
Collecting numexpr<3.0.0,>=2.8.4
Using cached numexpr-2.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (380 kB)
Collecting pydantic<2,>=1
Using cached pydantic-1.10.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.2 MB)
Collecting numpy<2,>=1
Using cached numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
Collecting tenacity<9.0.0,>=8.1.0
Using cached tenacity-8.2.2-py3-none-any.whl (24 kB)
Collecting multidict<7.0,>=4.5
Using cached multidict-6.0.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)
Collecting attrs>=17.3.0
Using cached attrs-23.1.0-py3-none-any.whl (61 kB)
Collecting charset-normalizer<4.0,>=2.0
Using cached charset_normalizer-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (199 kB)
Collecting yarl<2.0,>=1.0
Using cached yarl-1.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (269 kB)
Collecting frozenlist>=1.1.1
Using cached frozenlist-1.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (158 kB)
Collecting aiosignal>=1.1.2
Using cached aiosignal-1.3.1-py3-none-any.whl (7.6 kB)
Collecting typing-inspect>=0.4.0
Using cached typing_inspect-0.8.0-py3-none-any.whl (8.7 kB)
Collecting marshmallow<4.0.0,>=3.3.0
Using cached marshmallow-3.19.0-py3-none-any.whl (49 kB)
Collecting marshmallow-enum<2.0.0,>=1.5.1
Using cached marshmallow_enum-1.5.1-py2.py3-none-any.whl (4.2 kB)
Collecting packaging>=17.0
Using cached packaging-23.1-py3-none-any.whl (48 kB)
Collecting typing-extensions>=4.2.0
Using cached typing_extensions-4.6.0-py3-none-any.whl (30 kB)
Collecting idna<4,>=2.5
Using cached idna-3.4-py3-none-any.whl (61 kB)
Collecting urllib3<3,>=1.21.1
Using cached urllib3-2.0.2-py3-none-any.whl (123 kB)
Collecting certifi>=2017.4.17
Using cached certifi-2023.5.7-py3-none-any.whl (156 kB)
Collecting greenlet!=0.4.17
Using cached greenlet-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (610 kB)
Collecting mypy-extensions>=0.3.0
Using cached mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)
Installing collected packages: packaging, typing-extensions, mypy-extensions, multidict, marshmallow, idna, frozenlist, yarl, urllib3, typing-inspect, pydantic, numpy, marshmallow-enum, greenlet, charset-normalizer, certifi, attrs, async-timeout, aiosignal, tenacity, SQLAlchemy, requests, PyYAML, openapi-schema-pydantic, numexpr, dataclasses-json, aiohttp, langchain
Successfully installed PyYAML-6.0 SQLAlchemy-2.0.15 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 attrs-23.1.0 certifi-2023.5.7 charset-normalizer-3.1.0 dataclasses-json-0.5.7 frozenlist-1.3.3 greenlet-2.0.2 idna-3.4 langchain-0.0.177 marshmallow-3.19.0 marshmallow-enum-1.5.1 multidict-6.0.4 mypy-extensions-1.0.0 numexpr-2.8.4 numpy-1.24.3 openapi-schema-pydantic-1.2.4 packaging-23.1 pydantic-1.10.7 requests-2.31.0 tenacity-8.2.2 typing-extensions-4.6.0 typing-inspect-0.8.0 urllib3-2.0.2 yarl-1.9.2
```
</details>
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. install python==3.9.7 or 3.9.8 or 3.9.9 (with asdf or docker. I didn't checked the other versions.)
2. install langchain `pip install langchain`
3. see the error
```
Python 3.9.7 (default, May 23 2023, 11:05:54)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import langchain
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/agents/agent.py", line 16, in <module>
from langchain.agents.tools import InvalidTool
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/agents/tools.py", line 8, in <module>
from langchain.tools.base import BaseTool, Tool, tool
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/tools/__init__.py", line 42, in <module>
from langchain.tools.vectorstore.tool import (
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/tools/vectorstore/tool.py", line 13, in <module>
from langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/chains/__init__.py", line 2, in <module>
from langchain.chains.api.base import APIChain
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/chains/api/base.py", line 13, in <module>
from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/chains/api/prompt.py", line 2, in <module>
from langchain.prompts.prompt import PromptTemplate
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/prompts/__init__.py", line 3, in <module>
from langchain.prompts.chat import (
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/prompts/chat.py", line 10, in <module>
from langchain.memory.buffer import get_buffer_string
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/memory/__init__.py", line 28, in <module>
from langchain.memory.vectorstore import VectorStoreRetrieverMemory
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/memory/vectorstore.py", line 10, in <module>
from langchain.vectorstores.base import VectorStoreRetriever
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/vectorstores/__init__.py", line 2, in <module>
from langchain.vectorstores.analyticdb import AnalyticDB
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/vectorstores/analyticdb.py", line 16, in <module>
from langchain.embeddings.base import Embeddings
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/embeddings/__init__.py", line 19, in <module>
from langchain.embeddings.openai import OpenAIEmbeddings
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 67, in <module>
class OpenAIEmbeddings(BaseModel, Embeddings):
File "pydantic/main.py", line 197, in pydantic.main.ModelMetaclass.__new__
File "pydantic/fields.py", line 506, in pydantic.fields.ModelField.infer
File "pydantic/fields.py", line 436, in pydantic.fields.ModelField.__init__
File "pydantic/fields.py", line 552, in pydantic.fields.ModelField.prepare
File "pydantic/fields.py", line 663, in pydantic.fields.ModelField._type_analysis
File "pydantic/fields.py", line 808, in pydantic.fields.ModelField._create_sub_type
File "pydantic/fields.py", line 436, in pydantic.fields.ModelField.__init__
File "pydantic/fields.py", line 552, in pydantic.fields.ModelField.prepare
File "pydantic/fields.py", line 668, in pydantic.fields.ModelField._type_analysis
File "/home/takumi/.asdf/installs/python/3.9.7/lib/python3.9/typing.py", line 847, in __subclasscheck__
return issubclass(cls, self.__origin__)
TypeError: issubclass() arg 1 must be a class
>>>
```
<details>
<summary>with docker</summary>
```
$ docker run -it python:3.9.7-bullseye bash
$ pip install langchain
$ python -c "import langchain"
```
</details>
### Expected behavior
```
Python 3.10.1 (main, Dec 21 2021, 09:01:08) [GCC 10.2.1 20210110] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import langchain
>>>
```
- what to do?
- ~change [python dependency](https://github.com/hwchase17/langchain/blob/master/pyproject.toml#LL15C24-L15C24) to 3.10 or later~
- fix the version of typing_extensions to 4.5.0 or change the relevant code.
- Thank you for checking out this issue. If there are anything more to check, I would be glad to help. | import langchain with python<=3.9 fails | https://api.github.com/repos/langchain-ai/langchain/issues/5113/comments | 31 | 2023-05-23T02:38:48Z | 2023-12-29T18:37:48Z | https://github.com/langchain-ai/langchain/issues/5113 | 1,721,040,284 | 5,113 |
[
"langchain-ai",
"langchain"
] | ### System Info
LangChain 0.0.177
Python 3.10.9
Windows 10
### Who can help?
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
Copied directly from https://python.langchain.com/en/latest/modules/callbacks/getting_started.html 'Creating a Custom Handler'
```
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
class MyCustomHandler(BaseCallbackHandler):
def on_llm_new_token(self, token: str, **kwargs) -> None:
print(f"My custom handler, token: {token}")
# To enable streaming, we pass in `streaming=True` to the ChatModel constructor
# Additionally, we pass in a list with our custom handler
chat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[MyCustomHandler()])
chat([HumanMessage(content="Tell me a joke")])
```
### Expected behavior
Expected Behavior:
My custom handler, token:
My custom handler, token: Why
My custom handler, token: did
My custom handler, token: the
My custom handler, token: tomato
My custom handler, token: turn
My custom handler, token: red
My custom handler, token: ?
My custom handler, token: Because
My custom handler, token: it
My custom handler, token: saw
My custom handler, token: the
My custom handler, token: salad
My custom handler, token: dressing
My custom handler, token: !
My custom handler, token:
AIMessage(content='Why did the tomato turn red? Because it saw the salad dressing!', additional_kwargs={})
Error:
TypeError: Can't instantiate abstract class MyCustomHandler with abstract methods on_agent_action, on_agent_finish, on_chain_end, on_chain_error, on_chain_start, on_llm_end, on_llm_error, on_llm_start, on_text, on_tool_end, on_tool_error, on_tool_start | MyCustomHandler documentation example not working | https://api.github.com/repos/langchain-ai/langchain/issues/5112/comments | 2 | 2023-05-23T02:22:56Z | 2023-10-21T06:32:13Z | https://github.com/langchain-ai/langchain/issues/5112 | 1,721,028,735 | 5,112 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain 0.0.173
Python 3.10.11
openai 0.27.6
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from dotenv import load_dotenv
load_dotenv()
```
# user langchain llms
```python
from langchain.llms import AzureOpenAI
llm = AzureOpenAI(temperature=0.1,deployment_name="gpt-35-turbo",verbose=True)
```
```python
llm("who are you?")
```
```
' what do you want? why are you here? what do you want to do? what do you want to achieve? what do you want to be? what do you want to have? what do you want to give? what do you want to receive? what do you want to learn? what do you want to teach? what do you want to know? what do you want to see? what do you want to feel? what do you want to experience? what do you want to share? what do you want to create? what do you want to manifest? what do you want to change? what do you want to transform? what do you want to heal? what do you want to release? what do you want to forgive? what do you want to let go of? what do you want to embrace? what do you want to accept? what do you want to surrender? what do you want to allow? what do you want to receive? what do you want to give? what do you want to do? what do you want to be? what do you want to have? what do you want to create? what do you want to manifest? what do you want to experience? what do you want to share? what do you want to learn'
```
```python
llm("你是谁？")
```
```
'",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？",\n "你是谁？'
```
```python
from langchain.llms import OpenAI
llm_openai = OpenAI(engine="gpt-35-turbo")
llm_openai("who are you ?")
```
```
'” “I’m the devil,” said the Stranger. “My name is Legion.” In that moment, everything changed. The heat of the sun was extinguished in an instant, replaced by an iciness that flowed from the Stranger’s presence. The air itself seemed to grow thick and heavy, pressing down on me, as if I were drowning in molasses. My eyes were drawn back to the Stranger’s face, but this time I saw more than just a man. I saw something darker, something that made my soul cry out in despair. I saw a demon. I felt my body begin to shake, my fingers trembling uncontrollably. My mind was screaming at me to run, to flee from this place as fast as I could, but my feet seemed rooted to the spot. The Stranger’s smile grew wider, and it was all I could do to keep from screaming. “I have a proposition for you,” he said. “I’ve heard of your powers, and I’m impressed. I think you could be a great asset to me.” I tried to speak, but no sound came out. “Don’t be afraid,” he said. “I’m not here to hurt you. I’m here to offer you a deal. I can give you power'
```
# just use openai
```python
import openai
response = openai.ChatCompletion.create(
temperature=0.1,
engine="gpt-35-turbo", # engine = "deployment_name".
messages=[
{"role": "user", "content": """
who are you?
"""}, ]
)
# print(response)
print(response['choices'][0]['message']['content'])
```
```
I am an AI language model created by OpenAI, designed to assist with various tasks such as answering questions, generating text, and providing information.
```
### Expected behavior
is langchain make some settings when we call GPT?? | AzureOpenAI Repeat words | https://api.github.com/repos/langchain-ai/langchain/issues/5109/comments | 1 | 2023-05-23T01:06:06Z | 2023-07-11T10:01:58Z | https://github.com/langchain-ai/langchain/issues/5109 | 1,720,955,939 | 5,109 |
[
"langchain-ai",
"langchain"
] | ### Issue with current documentation:
Link: https://python.langchain.com/en/latest/integrations/aim_tracking.html
Below is the section that contains problematic code.
<img width="910" alt="Screenshot 2023-05-22 at 4 42 10 PM" src="https://github.com/hwchase17/langchain/assets/59850826/eb988031-4167-4a6c-a77f-9a0f25d2a736">
<br>
The document doesn't include library versions. I'm using `langchain==0.0.152`.
When I used `callbacks=callbacks`, this was my error:
<img width="592" alt="Screenshot 2023-05-22 at 4 42 37 PM" src="https://github.com/hwchase17/langchain/assets/59850826/6a3a46d5-cb43-4e88-be0e-a767d82064cd">
When I changed it to using `model_kwargs`, I got another error
<img width="430" alt="Screenshot 2023-05-22 at 4 43 41 PM" src="https://github.com/hwchase17/langchain/assets/59850826/e1ae42ee-1eac-4517-839f-224bfc507831">
It's unclear what key-value pairs `model_kwargs` is expecting. LangChain `callbacks` [docs](https://python.langchain.com/en/latest/modules/callbacks/getting_started.html) still use `callbacks=` throughout, rather than `model_kwargs`
### Idea or request for content:
Please either provide a working example of Aim+LangChain code or update the `callbacks` document if `callbacks` is indeed not relevant anymore. | DOC: Aim docs contains code that doesn't run | https://api.github.com/repos/langchain-ai/langchain/issues/5107/comments | 1 | 2023-05-22T23:51:58Z | 2023-09-10T16:13:45Z | https://github.com/langchain-ai/langchain/issues/5107 | 1,720,839,918 | 5,107 |
[
"langchain-ai",
"langchain"
] | ### Issue with current documentation:
What the docs show:
```python
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(deployment="your-embeddings-deployment-name")
```
But client is required, so you need
```python
from langchain.embeddings import OpenAIEmbeddings
import openai
embeddings = OpenAIEmbeddings(deployment="your-embeddings-deployment-name",client=openai.Embedding())
```
Similar situation for the docs
```python
model = AzureChatOpenAI(
openai_api_base=BASE_URL,
openai_api_version="2023-03-15-preview",
deployment_name=DEPLOYMENT_NAME,
openai_api_key=API_KEY,
openai_api_type = "azure",
)
```
When I really needed
```python
model = AzureChatOpenAI(
deployment_name="deployment-name", client=openai.ChatCompletion()
)
```
Might also be better (maybe the docs writer expected) that AzureChatOpenAI client default to openai.ChatCompletion() and OpenAIEmbeddings default client to be openai.Embedding().
### Idea or request for content:
_No response_ | DOC: client required but not in example code | https://api.github.com/repos/langchain-ai/langchain/issues/5105/comments | 2 | 2023-05-22T21:35:22Z | 2023-09-23T16:06:00Z | https://github.com/langchain-ai/langchain/issues/5105 | 1,720,596,854 | 5,105 |
[
"langchain-ai",
"langchain"
] | ### System Info
Hi
testing this loader, it looks as tho this is pulling trashed files from folders. I think this should be default to false if anything and be an opt in.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
### Related Components
- [X] Document Loaders
### Reproduction
use GoogleDriveLoader
1. point to folder
2. move a file to trash in folder
Reindex
File still can be searched in vector store.
### Expected behavior
Should not be searchable | GoogleDriveLoader seems to be pulling trashed documents from the folder | https://api.github.com/repos/langchain-ai/langchain/issues/5104/comments | 5 | 2023-05-22T21:21:14Z | 2023-05-25T05:26:19Z | https://github.com/langchain-ai/langchain/issues/5104 | 1,720,575,898 | 5,104 |
[
"langchain-ai",
"langchain"
] | ### System Info
Langchain Version: 0.0.175
Platform: macos
Python version: 3.9
Database: Postgres
### Who can help?
@vowelparrot @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Let's say we have two tables `users` & `profiles` and they are both have a column named `tags`.
2. SQLDatabaseChain produce the following query:
`SELECT "first_name", "last_name", "tags" FROM users INNER JOIN profiles ON users.id=profiles.user_id`
3. This generates the following exception:
`ProgrammingError('(psycopg2.errors.AmbiguousColumn) column reference "tags" is ambiguous
LINE 1: SELECT "first_name", "last_name", "tags")`
### Expected behavior
I would expect it to produce the query with table name specified, at least for the columns who share the same name across these these two tables.
I've tried to add enable use_query_checker but it didn't help. I also tried to add to the `QUERY_CHECKER` the following line:
`- Adding the table name when referencing columns that are specified in multiple tables (i.e. ambiguous column names)` - it didn't help either | psycopg2.errors.AmbiguousColumn exception when using SQLDatabaseChain | https://api.github.com/repos/langchain-ai/langchain/issues/5100/comments | 4 | 2023-05-22T17:05:51Z | 2023-10-20T07:27:37Z | https://github.com/langchain-ai/langchain/issues/5100 | 1,720,083,619 | 5,100 |
[
"langchain-ai",
"langchain"
] | def load_chain():
global j
os.environ["OPENAI_API_KEY"] =api_key[j%3]
print(api_key[j%3])
j=j+1
#global chain
print("模型加载开始")
#loader = DirectoryLoader('./langchian_gpt/yuliaoku', glob='**/*.txt')
loader = DirectoryLoader('./yuliaoku', glob='**/*.txt')
docs = loader.load()
# 文档切块
text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=0)
doc_texts = text_splitter.split_documents(docs)
# 调用openai Embeddings
embeddings = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"])
# 向量化
vectordb = Chroma.from_documents(doc_texts, embeddings)#
vectordb = Chroma.from_documents(doc_texts, embeddings, persist_directory="./yuliaoku")
vectordb.persist()
ll=ChatOpenAI(temperature=0.3, model_name="gpt-3.5-turbo", max_tokens=1024)
memory = ConversationBufferWindowMemory(memory_key="chat_history",k=3, output_key='answer',return_messages=True)
# 创建聊天机器人对象chain
chain = ConversationalRetrievalChain.from_llm(
retriever=vectordb.as_retriever(search_type="similarity",search_kwargs={"k": 1}),
llm=ll
verbose=True,
memory=memory,
get_chat_history=lambda h: h,
#map_reduce,map_rerank ,refine
return_source_documents=False)
print("模型加载结束")
return chain#,memory
def embedding(require_text):
chain=load_chain()
ans = chain({"question": require_text})
return ans['answer']
无法实现上下文功能，请问我哪里有问题，我的langchain是0.0161的版本 | I am still struggling. It does not remember anything in the chat history. What am I doing wrong? Here is my code: | https://api.github.com/repos/langchain-ai/langchain/issues/5099/comments | 2 | 2023-05-22T16:55:26Z | 2023-09-15T16:11:45Z | https://github.com/langchain-ai/langchain/issues/5099 | 1,720,063,351 | 5,099 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain 0.0.173
python 3.9.16
### Who can help?
@hwchase17 @agola11 @vowelparrot
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python3
custom_prompt_template = """Use the context to generate an appropriate reply to the query
Context: {context}
Query: {question}
Response:"""
CUSTOM_PROMPT = PromptTemplate(
template=learn_mode_prompt_template, input_variables=[
"context", "question"]
)
def generate_response(text: str, query: str):
retriever = create_document_vectorstore(
page_text=text)
chain_type_kwargs = {"prompt": CUSTOM_PROMPT }
qa = RetrievalQA.from_chain_type(llm=OpenAI(
openai_api_key=openai_api_key), chain_type="map_reduce", retriever=retriever)
qa.run(body.query)
```
### Expected behavior
tl;dr trying to use `RetrievalQA` chain with `chain_type` of `map_reduce` (and `refine`) errors out when using a custom prompt but successfully works with `chain_type=stuff`
Note this errors out with
```
ValidationError: 1 validation error for MapReduceDocumentsChain
prompt
extra fields not permitted (type=value_error.extra)
```
however if `chain_type` is changed to `stuff` the code generates a completion without a problem | `RetrievalQA` chain with chain_type `map_reduce` fails for custom prompts | https://api.github.com/repos/langchain-ai/langchain/issues/5096/comments | 12 | 2023-05-22T14:39:02Z | 2024-02-14T16:13:53Z | https://github.com/langchain-ai/langchain/issues/5096 | 1,719,836,908 | 5,096 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain 0.176.0
python 3.10
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
For example try running the following code:
```python
from langchain.agents import Tool
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.tools.python.tool import PythonREPLTool
from langchain.utilities import DuckDuckGoSearchAPIWrapper
import langchain
langchain.debug = True
search = DuckDuckGoSearchAPIWrapper()
tools = [
Tool(
name = "Current Search",
func=search.run,
description="useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term."
),
PythonREPLTool()
]
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm=ChatOpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
agent_chain("""create a regex that will extract titles from names. For example foo("Mrs. Smith") should return Mrs.""")
```
Debug of this code contains the following:
```
{
"generations": [
[
{
"text": "{\n \"action\": \"Python REPL\",\n \"action_input\": \"import re\\n\\nname = 'Mrs. Smith'\\n\\nregex = r'(Mr?s?\\.|Ms\\.|Miss|Dr\\.) ([A-Za-z]+)'\\n\\nmatch = re.search(regex, name)\\n\\nif match:\\n print(match.group(2))\"\n}",
"generation_info": null,
"message": {
"content": "{\n \"action\": \"Python REPL\",\n \"action_input\": \"import re\\n\\nname = 'Mrs. Smith'\\n\\nregex = r'(Mr?s?\\.|Ms\\.|Miss|Dr\\.) ([A-Za-z]+)'\\n\\nmatch = re.search(regex, name)\\n\\nif match:\\n print(match.group(2))\"\n}",
"additional_kwargs": {},
"example": false
}
}
]
],
"llm_output": {
"token_usage": {
"prompt_tokens": 555,
"completion_tokens": 76,
"total_tokens": 631
},
"model_name": "gpt-3.5-turbo"
}
}
```
Here you can see that this fails because the resulting answer contains escape characters that break json in the action input. This is a shortcoming of the json format. Changing the prompt and output parser to work with YAML works as expected.
### Expected behavior
I would expect to have output parser able to parse outputs containing escape characters. But this is a bit difficult with json. I propose we use yaml instead of json here. I am willing to develop a solution | Conversational Chat with PythonREPL tool breaks | https://api.github.com/repos/langchain-ai/langchain/issues/5094/comments | 1 | 2023-05-22T14:20:57Z | 2023-09-10T16:13:54Z | https://github.com/langchain-ai/langchain/issues/5094 | 1,719,805,562 | 5,094 |
[
"langchain-ai",
"langchain"
] | ### System Info
**LangChain version:** 0.0.176
**Platform:** Local Ubuntu 22.04
**Python version:** 3.10
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is the code example I'm currently using. It's a slightly modified version of [LangChain summarize docs](https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html)
```py
from pdfminer.high_level import extract_text
from langchain.llms import OpenAI
from langchain.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
text_splitter = CharacterTextSplitter()
def file_summarizer(file):
read_file = extract_text(file.name)
texts = text_splitter.split_text(read_file)
docs = [Document(page_content=t.replace("\t", " ").strip()) for t in texts[:1]]
llm = OpenAI(temperature=0, max_tokens=2000)
for d in docs:
print(llm.get_num_tokens(d.page_content)) # This prints 1054
chain = load_summarize_chain(llm, chain_type="map_reduce")
result = chain.run(docs).strip()
print(llm.get_num_tokens(result)) # This prints 87
return result
```
### Expected behavior
It looks like it's ignoring the value of the `max_tokens` parameter (or that I'm misunderstanding that it does), but no matter what value I pass to that parameter, I always receive very short answers, no matter the token size of the prompt.
`text-davinci-003` has 4097 tokens, which means that for my previous example, I should've had around ~3000 tokens available for my summary, however, I just received 87.
Am I missing or misunderstanding any bit of documentation?
Thanks in advance! | load_summarize_chain doesn't return the specified max_tokens in OpenAI LLM | https://api.github.com/repos/langchain-ai/langchain/issues/5093/comments | 1 | 2023-05-22T14:09:13Z | 2023-05-29T13:38:29Z | https://github.com/langchain-ai/langchain/issues/5093 | 1,719,782,534 | 5,093 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I am using PlanAndExecuteAgent to answer a complex statement which has multiple placeholders, the placeholders need to be filled from executing multiple sql queries. I tried using planAndExecuteAgent but results were not fruitful. I took planAndExecuteAgent only for planning and used `create_sql_agent` to execute the plan coming out of planAndExecuteAgent. This worked out decently good.
I debugged a little bit and found that the context information I gave as part of initial query is lost and not being used in execute steps. I think that context information need to be provided to steps as well so they can execute better.
One more observation is when sending calls to llm for planning it doesn't have the sql tool context where as while executing it doesn't have input context. This mismatch is causing the issue I think.
This is my observation. Please suggest what is the right way to use PlanAndExecuteAgent directly.
### Suggestion:
_No response_ | Issue: Issue with PlanAndExecuteAgent | https://api.github.com/repos/langchain-ai/langchain/issues/5087/comments | 1 | 2023-05-22T11:11:07Z | 2023-09-10T16:14:00Z | https://github.com/langchain-ai/langchain/issues/5087 | 1,719,462,590 | 5,087 |
[
"langchain-ai",
"langchain"
] | ### Feature request
Hi maintainer,
In my case, a chain runs for a long time, multiple actions will be executed during this process. The main reason is that there will be multiple conversations with the LLMs.
Now, the `agent.run(...)` returns final value and **intermediate steps** with following setting:
```python
agent = create_pandas_dataframe_agent(openai_llm, df, return_intermediate_steps=True, verbose=True)
agent("some user query")
```
This is very useful for obtaining intermediate steps to enable front-end users to understand ideas from LLMs but they have to wait for it complete, this is not friendly to them.
I understand that the process of executing a chain is like a continuous process of generating and executing `Action`, which is in line with the idea of `generator` in Python, where we can `yield` completed actions during the running process.
Intuitively, it's like this:
https://github.com/hwchase17/langchain/blob/ef7d015be566588b3263ee6ee1259a30ee53212c/langchain/agents/agent.py#L946C63-L959
```python
yield next_step_output # line 954
```
### Motivation
Enable front-end(some web apps using langchain) users to understand the `chain's` thinking process while waiting for results , and in a more intuitive/simple way compared to callback for developer.
### Your contribution
NOT Now | Yield intermediate steps during the chain execution process | https://api.github.com/repos/langchain-ai/langchain/issues/5086/comments | 5 | 2023-05-22T09:37:01Z | 2023-11-10T11:26:31Z | https://github.com/langchain-ai/langchain/issues/5086 | 1,719,303,615 | 5,086 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain==0.0.165
Python 3.10.8
macos m2
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
def __init__(self, prompt_template: PromptTemplate):
self.llmchainList = []
key_list = self.__getAPIKeys()
for key in key_list:
llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=key, max_tokens = 1500)
llm_chain = LLMChain(
llm=llm,
prompt=prompt_template)
self.llmchainList.append(llm_chain)
def __pickupLlm(self):
chain = self.llmchainList.pop(0)
self.llmchainList.append(chain)
return chain
def send(self, **kwargs):
chain = self.__pickupLlm()
print("send message to chatgpt...")
res = {}
with get_openai_callback() as cb:
res = chain.run(**kwargs)
print(cb)
print("chatgpt said...")
print(res)
```
The code is very straight forward, I have a api token list, when init, create a `llmchain` list.
when request to `chatgpt`, I pop up one `llmchain` in the list (simple load balance for rate limit) .
But, when I start request with multiple thread, I got many `rate limit` exception event I sleep enough ms between request.
and I found all `rate limit` exception point to one api token(`Rate limit reached for default-gpt-3.5-turbo in organization org-WN9k0BUSqN4pNvlU5N6T74Yq on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.`), also the api token on the end of my list. that's weird. My simple load balance will never pick on one "api token" in one batch request.
Even I print `key` of each `llmchain`, it'a correct, for example, one batch, I send 3 request, and each `llmchian` with different `apikey`, but I still get `rate limit` with one specific organization which is exactly in the end of my `api token` list.
So I have to suspect there must be some wrong with `LLMCHAIN`, expect reply, thanks
### Expected behavior
By design, no rate limit exception | llmchain not work as expect in multiple thread scenario | https://api.github.com/repos/langchain-ai/langchain/issues/5084/comments | 5 | 2023-05-22T09:00:40Z | 2023-09-19T16:09:35Z | https://github.com/langchain-ai/langchain/issues/5084 | 1,719,236,879 | 5,084 |
[
"langchain-ai",
"langchain"
] | ### System Info
v0.0.176
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The default QA with Sources Chain: langchain/chains/qa_with_sources/stuff_prompt.py
causes issues with Azure Content Filter, which will return the following:
```
"error": {
"message": "The response was filtered",
"type": null,
"param": "prompt",
"code": "content_filter",
"status": 400
}
```
I believe this is due to the sections that mention issues like covid, which is found in the example source content in lines 22-28.
I modified to the following to resolve the issue:
```
Content: Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.
Source: 0-pl
Content: And we won’t stop.
Source: 24-pl
Content: And a proud Ukrainian people, I want you to know that we are going to be okay.
Source: 5-pl
Content: More support for patients and families. Our future is forged. \n\nWell I know this nation.
Source: 34-pl
```
this is probably not the best version of the source content, but it worked for me.
### Expected behavior
The QA prompt should not trigger Azure Content Filtering. | Azure Content Filtering on Default QA with sources prompt. | https://api.github.com/repos/langchain-ai/langchain/issues/5082/comments | 2 | 2023-05-22T07:16:39Z | 2023-09-15T16:11:55Z | https://github.com/langchain-ai/langchain/issues/5082 | 1,719,055,182 | 5,082 |
[
"langchain-ai",
"langchain"
] | I need to limit the number of documents that AzureCognitiveSearchRetriever returns so that I can aggregate only the most relevant documents. Is there a way to do this with the current functionality or do we need to implement it? | Issue: Can we limit the number of relevant documents returned by AzureCognitiveSearchRetriever? | https://api.github.com/repos/langchain-ai/langchain/issues/5081/comments | 16 | 2023-05-22T06:10:42Z | 2023-07-27T05:36:05Z | https://github.com/langchain-ai/langchain/issues/5081 | 1,718,965,240 | 5,081 |
[
"langchain-ai",
"langchain"
] | ### Feature request
Add capability to generate and run python code using langchain. I created a [github repo](https://github.com/thismlguy/code-genie) called code-genie to support this. Here's a [starter notebook](https://code-genie.readthedocs.io/en/main/notebooks/Google%20Analytics%20Pipeline%20Example.html) for it. I want to build that functionality into langchain itself for wider adoption.
### Motivation
A lot of data scientists/business analysts are using GPT3.5/4 API to generate code for ad-hoc analysis. But they end up copy pasting code from chatgpt interface into their notebooks and spend time making it work with their own variables.
### Your contribution
I can create a PR introducing a chain with does this once I get approval from the maintainers that they are open to merging this feature in. | Chain for generating and running python code | https://api.github.com/repos/langchain-ai/langchain/issues/5080/comments | 1 | 2023-05-22T05:40:25Z | 2023-09-10T16:14:10Z | https://github.com/langchain-ai/langchain/issues/5080 | 1,718,933,305 | 5,080 |
[
"langchain-ai",
"langchain"
] | When I use CosmosDBChatMessageHistory, the conversation history is stored in DB, but every time load message returns None, and each time I run add_user_message, I perform a replace operation, not an increment on the original record.
| CosmosDBChatMessageHistory.load_messages return None | https://api.github.com/repos/langchain-ai/langchain/issues/5077/comments | 4 | 2023-05-22T02:18:01Z | 2023-11-20T16:06:46Z | https://github.com/langchain-ai/langchain/issues/5077 | 1,718,767,157 | 5,077 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I'm seeing the official documentation vectorstore Chromadb and I found way to do query using filters where or where_documents.
I tried to use filter using the langchain chromadb wrapper by examples and I don't see how to do query using filters.
Exists any way to do filters if no, We could implement new features to do this!
https://docs.trychroma.com/usage-guide#querying-a-collection
### Suggestion:
_No response_ | Issue: Create way to do filters using VectorStore | https://api.github.com/repos/langchain-ai/langchain/issues/5076/comments | 2 | 2023-05-22T01:42:22Z | 2023-09-10T16:14:16Z | https://github.com/langchain-ai/langchain/issues/5076 | 1,718,736,987 | 5,076 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
Agent always translate my question to English then use a tool, when I use ChatOpenAI with default model gpt-3.5-turbo.
But not translate to English with gpt-4 model.
```
"""Create a ChatVectorDBChain for question/answering."""
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.vectorstores.base import VectorStore
from langchain.utilities import GoogleSerperAPIWrapper
import os
from langchain.agents import Tool
from langchain.agents import initialize_agent,AgentType,AgentExecutor
from langchain.chains import RetrievalQA
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.llm import LLMChain
from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT
from langchain.chains.question_answering import load_qa_chain
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.utilities.zapier import ZapierNLAWrapper
def get_agent(
chain_type: str, vcs_swft: VectorStore,vcs_path: VectorStore, agent_cb_handler) -> AgentExecutor:
agent_cb_manager = AsyncCallbackManager([agent_cb_handler])
llm = ChatOpenAI(
# model_name="gpt-4",
temperature=0,
verbose=True,
# request_timeout=120,
)
llm_qa = ChatOpenAI(
temperature=0,
verbose=True,
# request_timeout=120,
)
search = GoogleSerperAPIWrapper()
doc_search_swft = RetrievalQA.from_chain_type(llm=llm_qa, chain_type=chain_type, retriever=vcs_swft.as_retriever())
doc_search_path = RetrievalQA.from_chain_type(llm=llm_qa, chain_type=chain_type, retriever=vcs_path.as_retriever())
# doc_search = get_qa_chain(chain_type=chain_type,vectorstore=vectorstore)
# zapier = ZapierNLAWrapper()
# toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
tools = [
Tool(
name = "QA SWFT System",
func=doc_search_swft.run,
description="useful for when you need to answer questions about swft. Input should be a fully formed question.",
coroutine=doc_search_swft.arun
),
Tool(
name = "QA Metapath System",
func=doc_search_path.run,
description="useful for when you need to answer questions about metapath. Input should be a fully formed question.",
coroutine=doc_search_path.arun
),
Tool(
name = "Current Search",
func=search.run,
description="""
useful for when you need to answer questions about current events or the current state of the world or you need to ask with search.
the input to this should be a single search term.
""",
coroutine=search.arun
),
]
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent = initialize_agent(tools=tools, llm=llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory,callback_manager=agent_cb_manager)
return agent
```
console print:
```
INFO: Will watch for changes in these directories: ['/home/ec2-user/chatbot-agent']
INFO: Uvicorn running on http://:9002 (Press CTRL+C to quit)
INFO: Started reloader process [1254384] using WatchFiles
INFO: Started server process [1254386]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: 103.116.245.58:0 - "GET / HTTP/1.0" 200 OK
INFO: ('172.71.218.188', 0) - "WebSocket /chat" [accepted]
INFO: connection open
ON_CHAIN_START: Inputs: {'input': 'btc现在的价格是多少美元？', 'chat_history': []}
> Entering new AgentExecutor chain...
ON_CHAIN_START: Inputs: {'input': 'btc现在的价格是多少美元？', 'chat_history': [], 'agent_scratchpad': [], 'stop': ['\nObservation:', '\n\tObservation:']}
ON_CHAIN_END: Outputs: {'text': '{\n "action": "Current Search",\n "action_input": "btc price usd"\n}'}
ON_AGENT_ACTION: tool: Current Search
{
"action": "Current Search",
"action_input": "btc price usd"
}
Observation: 26,667.50 United States Dollar
Thought:ON_CHAIN_START: Inputs: {'input': 'btc现在的价格是多少美元？', 'chat_history': [], 'agent_scratchpad': [AIMessage(content='{\n "action": "Current Search",\n "action_input": "btc price usd"\n}', additional_kwargs={}, example=False), HumanMessage(content="TOOL RESPONSE: \n---------------------\n26,667.50 United States Dollar\n\nUSER'S INPUT\n--------------------\n\nOkay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else.", additional_kwargs={}, example=False)], 'stop': ['\nObservation:', '\n\tObservation:']}
ON_CHAIN_END: Outputs: {'text': '{\n "action": "Final Answer",\n "action_input": "The current price of BTC in USD is 26,667.50."\n}'}
ON_AGENT_FINISH: {'output': 'The current price of BTC in USD is 26,667.50.'}
{
"action": "Final Answer",
"action_input": "The current price of BTC in USD is 26,667.50."
}
ON_CHAIN_END: Outputs: {'output': 'The current price of BTC in USD is 26,667.50.'}
> Finished chain.
Result: The current price of BTC in USD is 26,667.50.
INFO: connection closed
```
### Suggestion:
_No response_ | Issue: Agent always translate my question to English then use a tool, when I use ChatOpenAI with default model gpt-3.5-turbo. | https://api.github.com/repos/langchain-ai/langchain/issues/5075/comments | 3 | 2023-05-22T01:32:56Z | 2023-09-19T16:09:41Z | https://github.com/langchain-ai/langchain/issues/5075 | 1,718,728,962 | 5,075 |
[
"langchain-ai",
"langchain"
] | ### System Info
Django backend that is using an `AsyncWebsocketConsumer`.
Goal: Stream LLM messages to a frontend React client via an `AsyncWebsocketConsumer`, using a custom `AsyncCallbackHandler`.
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm using the async handler to stream the LLM's output from a Django backend that is using an `AsyncWebsocketConsumer`.
I'm trying to stream the `ChatOpenAI` LLM's output via a custom subclass of the `AsyncCallbackHandler` (`streaming` is set to `True`).
However, the async `on_llm_new_token` function is never being called.
Unsure what the issue may be, as extending the `BaseCallbackHandler` works well for writing to stdout.
Here's the code that's not being called in the async on_llm_new_token: https://github.com/virattt/chat_app/pull/5/files#diff-176a7b37329b8a2846ff511f4dc15edd7d17d1478e7b4742f97700d97319b578R101-R103
Any thoughts or suggestions would be helpful.
### Expected behavior
The async `on_llm_new_token` function should get called when a new token is received from the LLM. | [AsyncCallbackHandler] on_llm_new_token not being called | https://api.github.com/repos/langchain-ai/langchain/issues/5074/comments | 5 | 2023-05-22T01:29:51Z | 2023-08-14T04:55:22Z | https://github.com/langchain-ai/langchain/issues/5074 | 1,718,726,643 | 5,074 |
[
"langchain-ai",
"langchain"
] | ### Feature request
Qdrant allows you to set the conditions to be used when searching or retrieving points. The filter is passed as **_MetadataFilter_** right now. Can we pass rest.Filter directly so that we can utilize all the filters provided by Qdrant.
```
def _qdrant_filter_from_dict(self, filter: Optional[MetadataFilter]) -> Any:
if not filter:
return None
from qdrant_client.http import models as rest
return rest.Filter(
must=[
condition
for key, value in filter.items()
for condition in self._build_condition(key, value)
]
)
```
### Motivation
I'm frustrated with how to only talk to a few document ingested in Qdrant. From my understanding, the current implementation only allows you to perform 'and' operation among the filtering metadatas. Is it able to perform 'or' operation?
What I have:
```
retriever=qdrant_store.as_retriever(
search_kwargs={
"filter": {"source_file":"file_1.md"}
}
),
```
What I want:
```
retriever=qdrant_store.as_retriever(
search_kwargs={
rest.Filter(
must=[
rest.FieldCondition(
key="source_file",
match=rest.MatchAny(any=["file_1.md", "file_2.md"]),
)
]
)
}
),
```
### Your contribution
N/A | [Question] How to use Qdrant MatchAny filter? | https://api.github.com/repos/langchain-ai/langchain/issues/5073/comments | 2 | 2023-05-22T01:16:59Z | 2023-08-17T05:02:54Z | https://github.com/langchain-ai/langchain/issues/5073 | 1,718,718,602 | 5,073 |
[
"langchain-ai",
"langchain"
] | ### Feature request
Weaviate has the option to pass _additional field while executing a query
https://weaviate.io/developers/weaviate/api/graphql/additional-properties
It would be good to be able to use this feature and add the response to the results. It is a small change, without breaking the API. We can use the kwargs argument, similar to where_filter in the python class weaviate.py.
### Motivation
When comparing and understanding query results, using certainty is a good way.
### Your contribution
I like to contribute to the PR. As it would be my first contribution, I need to understand the integration tests and build the project, and I already tested the change in my local code sample. | Add option to use _additional fields while executing a Weaviate query | https://api.github.com/repos/langchain-ai/langchain/issues/5072/comments | 0 | 2023-05-21T22:37:40Z | 2023-05-23T01:57:11Z | https://github.com/langchain-ai/langchain/issues/5072 | 1,718,663,745 | 5,072 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
Looks like the inference time of the LLamacpp model is a lot slower when using LlamaCpp wrapper (compared to the llama-cpp original wrapper).
Here are the results for the same prompt on the RTX 4090 GPU.
When using llamacpp-python Llama wrapper directly:
When using langchain LlamaCpp wrapper:
As you can see, it takes nearly 12x more time for the prompt_eval stage (2.67 ms per token vs 35 ms per token)
Am i missing something? In both cases, the model is fully loaded to the GPU. In the case of the Langchain wrapper, no chain was used, just direct querying of the model using the wrapper's interface. Same parameters.
Link to the example notebook (values are a lil different, but the problem is the same): https://github.com/mmagnesium/personal-assistant/blob/main/notebooks/langchain_vs_llamacpp.ipynb
Appreciate any help.
### Suggestion:
Unfortunately, no suggestion, since i don't understand whats the problem. | Issue: LLamacpp wrapper slows down the model | https://api.github.com/repos/langchain-ai/langchain/issues/5071/comments | 5 | 2023-05-21T21:49:24Z | 2023-05-27T15:40:58Z | https://github.com/langchain-ai/langchain/issues/5071 | 1,718,651,178 | 5,071 |
[
"langchain-ai",
"langchain"
] | ### Issue with current documentation:
Hello
I was trying to reproduce an example from that documentation
https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html?highlight=SQLDatabaseToolkit#
and got error
toolkit = SQLDatabaseToolkit(db=db )
--
ValidationError: 1 validation error for SQLDatabaseToolkit
llm
field required (type=value_error.missing)
--
### Idea or request for content:
_No response_ | DOC: SQL Database Agent | https://api.github.com/repos/langchain-ai/langchain/issues/5068/comments | 8 | 2023-05-21T19:43:41Z | 2023-11-06T16:08:09Z | https://github.com/langchain-ai/langchain/issues/5068 | 1,718,618,387 | 5,068 |
[
"langchain-ai",
"langchain"
] | ### System Info
LangChain v0.0.171
ChromaDB v0.3.22
Python v3.10.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is my code:
```
def askQuestion(self, collection_id, question):
collection_name = "collection-" + str(collection_id)
self.llm = ChatOpenAI(model_name=self.model_name, temperature=self.temperature, openai_api_key=os.environ.get('OPENAI_API_KEY'), streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key='answer')
chroma_Vectorstore = Chroma(collection_name=collection_name, embedding_function=self.embeddingsOpenAi, client=self.chroma_client)
self.chain = ConversationalRetrievalChain.from_llm(self.llm, chroma_Vectorstore.as_retriever(),
return_source_documents=True,verbose=VERBOSE,
memory=self.memory)
result = self.chain({"question": question})
print(result)
res_dict = {
"answer": result["answer"],
}
res_dict["source_documents"] = []
for source in result["source_documents"]:
res_dict["source_documents"].append({
"page_content": source.page_content,
"metadata": source.metadata
})
return res_dict
```
### Expected behavior
When I print the result directly after `result = self.chain({"question": question})`, I get displayed sources, metadata, kwargs, question, chat_history.
I see here: https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/chroma.py#L165 and in line 182 in the official source code, that the similarity_search_with_score() is being called by default.
How can I also display the score than? | ConversationalRetrievalChain doesn't return score with sources | https://api.github.com/repos/langchain-ai/langchain/issues/5067/comments | 21 | 2023-05-21T18:40:34Z | 2024-01-11T08:20:25Z | https://github.com/langchain-ai/langchain/issues/5067 | 1,718,600,362 | 5,067 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain 0.0.173
faiss-cpu 1.7.4
python 3.10.11
Void linux
### 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
It's a logic error in langchain.vectorstores.faiss.__add()
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L94-L100
https://github.com/hwchase17/langchain/blob/0c3de0a0b32fadb8caf3e6d803287229409f9da9/langchain/vectorstores/faiss.py#L118-L126
The id is not possible to specify as a function argument. This makes it impossible to detect duplicate additions, for instance.
### Expected behavior
It should be possible to specify id of inserted documents / texts using the add_documents / add_texts methods, as it is in the Chroma object's methods.
As a side-effect this ability would also fix the inability to remove duplicates (see https://github.com/hwchase17/langchain/issues/2699 and https://github.com/hwchase17/langchain/issues/3896 ) by the approach of using ids unique to the content (I use a hash, for example). | FAISS should allow you to specify id when using add_text | https://api.github.com/repos/langchain-ai/langchain/issues/5065/comments | 6 | 2023-05-21T16:39:28Z | 2023-05-25T05:26:48Z | https://github.com/langchain-ai/langchain/issues/5065 | 1,718,564,503 | 5,065 |
[
"langchain-ai",
"langchain"
] | https://github.com/hwchase17/langchain/blob/424a573266c848fe2e53bc2d50c2dc7fc72f2c15/langchain/vectorstores/chroma.py#L275
The line above would only select the candidates based on MMR, and not reorder them based on MMR's ranking.
This is not the case for any other vectorstores that support MMR.
cc @hwchase17
| [Bug] MMR ordering is not preserved for the Chroma Vectorstore | https://api.github.com/repos/langchain-ai/langchain/issues/5061/comments | 3 | 2023-05-21T15:50:19Z | 2023-09-17T17:16:54Z | https://github.com/langchain-ai/langchain/issues/5061 | 1,718,549,470 | 5,061 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I am using the babyagi with the zeroshotagent the prompt length error is persistent , how can we fix it possibly summarise the agent_scratchpad and if someone could explain how does it work by default ? does It keep appending the bot and human response in it or just the previous ones?
### Suggestion:
_No response_ | Issue: How do I fix the prompt length error in any agent that uses LLMCHAIN ? | https://api.github.com/repos/langchain-ai/langchain/issues/5057/comments | 2 | 2023-05-21T12:42:59Z | 2023-09-10T16:14:20Z | https://github.com/langchain-ai/langchain/issues/5057 | 1,718,490,382 | 5,057 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
By way of example from the official docs,
memory = ConversationTokenBufferMemory(llm=llm, max_token_limit=10)
memory. save_context({"input": "hi"}, {"ouput": "what's up"})
memory. save_context({"input": "not much you"}, {"ouput": "not much"})
memory. load_memory_variables({})
If it is the memory method, when the number of memories exceeds the given token limit, it will trim the memory normally, but if persistent messages are added, the memory will not be trimmed, for example
history = RedisChatMessageHistory(
url=redis_url, ttl=600, session_id='id') memory = ConversationTokenBufferMemory(llm=llm, max_token_limit=10, chat_memory=history)
memory. save_context({"input": "hi"}, {"ouput": "what's up"})
memory. save_context({"input": "not much you"}, {"ouput": "not much"})
memory. load_memory_variables({})
You will find that the limit of the token exceeded in time, or it will print out all the historical memory
This will cause your code to fail to work properly for a certain period of time until the ttl that triggers redis expires
At present, my approach is to redefine a TokenMemory class. The following code is for reference only. If you have a better way to implement it, you are welcome to discuss it
`class CustomTokenMemory(BaseChatMemory):
new_buffer: List = []
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm:BaseLanguageModel
memory_key: str = "history"
max_token_limit: int = 2000
@property
def buffer(self) -> List[BaseMessage]:
"""String buffer of memory."""
if not self. new_buffer:
self. prune_memory()
return self. new_buffer
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self. memory_key]
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
buffer: Any = self.buffer
if self. return_messages:
final_buffer: Any = buffer
else:
final_buffer = get_buffer_string(
buffer,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
return {self. memory_key: final_buffer}
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer. Pruned."""
super(). save_context(inputs, outputs)
self. prune_memory()
def prune_memory(self):
# Prune buffer if it exceeds max token limit
buffer = self.chat_memory.messages
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
if curr_buffer_length > self.max_token_limit:
while curr_buffer_length > self.max_token_limit:
buffer. pop(0)
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
self.new_buffer = buffer`
Here I newly define a new_buffer array to point to the pruned memory, because in ConversationTokenBufferMemory, his self.buffer always gets all the messages in redis, so even if the memory is pruned in save_context, but All the historical messages obtained through load_memory_variables. My change here is that when the new_buffer is empty, the historical messages are first obtained from redis. Here, it needs to be trimmed once to ensure that the obtained messages must be within the Token limit, so The main purpose is to deal with rare scenarios, such as when your service is down or restarted, and the messages in the memory disappear. Although this is not necessary in most cases, then after new messages enter, the buffer will be deleted. new_buffer instead, then the pruned memory is obtained through load_memory_variables
### Suggestion:
_No response_ | The problem that ConversationTokenBufferMemory cannot be trimmed normally due to the use of persistent messages | https://api.github.com/repos/langchain-ai/langchain/issues/5053/comments | 4 | 2023-05-21T10:41:45Z | 2024-05-20T08:01:06Z | https://github.com/langchain-ai/langchain/issues/5053 | 1,718,455,217 | 5,053 |
[
"langchain-ai",
"langchain"
] | ### System Info
I'm working on Q&A using OpenAI for pdf and another documents. Below is the code
```
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain import OpenAI, VectorDBQA
import pickle
import textwrap
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
import os
import warnings
warnings.filterwarnings("ignore")
# Set up the environment variable for the OpenAI API key
os.environ["OPENAI_API_KEY"] = ""
def get_documents(folder_path, file_extension):
documents = []
if file_extension == 'pdf':
pdf_loader = DirectoryLoader(folder_path, glob="./*.pdf", loader_cls=PyPDFLoader) # Select PDF files
documents += pdf_loader.load()
elif file_extension == 'txt':
txt_loader = DirectoryLoader(folder_path, glob="./*.txt") # Select TXT files
documents += txt_loader.load()
elif file_extension == 'combined':
pdf_loader = DirectoryLoader(folder_path, glob="./*.pdf", loader_cls=PyPDFLoader) # Select PDF files
documents += pdf_loader.load()
txt_loader = DirectoryLoader(folder_path, glob="./*.txt") # Select TXT files
documents += txt_loader.load()
else:
return None
return documents
def get_query_result(query, documents):
# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)
# Query documents
embeddings = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_API_KEY'])
docsearch = Chroma.from_documents(texts, embeddings)
qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=docsearch, return_source_documents=True)
result = qa({"query": query})
result_text = result['result'].strip()
source = result.get('source_documents', [{}])[0].metadata.get('source', '')
page = result.get('source_documents', [{}])[0].metadata.get('page', '')
return result_text, source, page
def chat_loop(file_extension, folder_path):
documents = get_documents(folder_path, file_extension)
if documents is None:
print("Invalid folder path or no supported files found.")
return
while True:
query = input("Enter your query (type 'exit' to end): ")
if query.lower() == 'exit':
break
result = get_query_result(query, documents)
if result is not None:
result_text, source, page = result
print("Result:", result_text)
if source:
print("Source:", source)
print("Page:", page)
else:
print("No answer found for the query.")
print() # Print an empty line for separation
# Get the selected file extension and folder path from the webpage
selected_file_extension = 'combined'
folder_path = 'Documents'
# Start the chat loop
chat_loop(selected_file_extension, folder_path)
```
The code above will just take the input pdf or any other document text and provide a single line answer. In ChatGPT, if we provide it a long text or paragraph and ask it a question, it will give us the answer and explain where it got the answer and why it is correct. Is it possible to perform the same in the above code?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Looking for a better explanation of answer instead of returning a single line answer or just answer.
### Expected behavior
Expecting to return the answers with better explanation or articulation. | Regarding explaination of answer which is returned by OpenAI embeddings | https://api.github.com/repos/langchain-ai/langchain/issues/5052/comments | 1 | 2023-05-21T10:11:49Z | 2023-09-10T16:14:25Z | https://github.com/langchain-ai/langchain/issues/5052 | 1,718,447,024 | 5,052 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
Hey,
I am willing to use { and } chars as an example in my prompt template (I want to prompt my chain to generate css code), but this generates this error:
```
Traceback (most recent call last):
File "/Users/thomas/workspace/j4rvis/j4rvis/tools/document_tools.py", line 43, in <module>
prompt=PromptTemplate(
^^^^^^^^^^^^^^^
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for PromptTemplate
__root__
Invalid prompt schema; check for mismatched or missing input parameters. ' ' (type=value_error)
```
How could I escape those chars so they don't get interpreted as input_variables?
### Suggestion:
_No response_ | Issue: Escaping { and } chars in a langchain.prompts.PromptTemplate | https://api.github.com/repos/langchain-ai/langchain/issues/5051/comments | 4 | 2023-05-21T09:25:49Z | 2023-09-22T16:08:39Z | https://github.com/langchain-ai/langchain/issues/5051 | 1,718,432,064 | 5,051 |
[
"langchain-ai",
"langchain"
] | ### System Info
MacOS Ventura, zshell
`pyenv virtualenv 3.11 langchain`, clean environment
`pyenv local langchain`, automatically activate environment
`curl -sSL https://install.python-poetry.org | python -`, install poetry
`export PATH="/Users/steven/.local/bin:$PATH`, added this to allow for poetry to be accessible
`pip install --upgrade pip`
`pip install torch`
`poetry install -E all`
yields the following output. Strange.
```bash
• Installing torch (1.13.1): Failed
RuntimeError
Unable to find installation candidates for torch (1.13.1)
at ~/Library/Application Support/pypoetry/venv/lib/python3.11/site-packages/poetry/installation/chooser.py:76 in choose_for
72│
73│ links.append(link)
74│
75│ if not links:
→ 76│ raise RuntimeError(f"Unable to find installation candidates for {package}")
77│
78│ # Get the best link
79│ chosen = max(links, key=lambda link: self._sort_key(package, link))
80│
```
### Who can help?
@hwchase17 This is install related and while I have langchain running fine from pip in another virtual environment running the same python version (3.11.3), things with poetry are not working well.
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
MacOS Ventura, zshell
`pyenv virtualenv 3.11 langchain`, clean environment
`pyenv local langchain`, automatically activate environment
`curl -sSL https://install.python-poetry.org | python -`, install poetry
`export PATH="/Users/steven/.local/bin:$PATH`, added this to allow for poetry to be accessible
`pip install --upgrade pip`
`pip install torch`
`poetry install -E all`
### Expected behavior
```bash
• Installing torch (1.13.1): Failed
RuntimeError
Unable to find installation candidates for torch (1.13.1)
at ~/Library/Application Support/pypoetry/venv/lib/python3.11/site-packages/poetry/installation/chooser.py:76 in choose_for
72│
73│ links.append(link)
74│
75│ if not links:
→ 76│ raise RuntimeError(f"Unable to find installation candidates for {package}")
77│
78│ # Get the best link
79│ chosen = max(links, key=lambda link: self._sort_key(package, link))
80│
``` | poetry install -E all` fails because `torch` undetected | https://api.github.com/repos/langchain-ai/langchain/issues/5048/comments | 10 | 2023-05-21T05:19:15Z | 2024-02-13T16:16:32Z | https://github.com/langchain-ai/langchain/issues/5048 | 1,718,375,450 | 5,048 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
Hi,
for the following code there is a dimension error -> InvalidDimensionException: Dimensionality of (1536) does not match index dimensionality (384)
```embeddingsFunction = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1)
persist_directory = "chromaDB_csv"
vectordb = None
vectordb = Chroma.from_documents(
documents=docs, embeddings=embeddingsFunction, persist_directory=persist_directory
)
vectordb.persist()
AzureOpenAI.api_type = "azure"
llm = AzureChatOpenAI(
deployment_name="gpt-35-turbo",
engine="gpt-35-turbo",
openai_api_base=os.getenv('OPENAI_API_BASE'),
openai_api_key=os.getenv("OPENAI_API_KEY"),
openai_api_type = "azure",
openai_api_version = "2023-03-15-preview"
)
print(os.getenv('OPENAI_API_BASE'))
print(os.getenv('OPENAI_API_KEY'))
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddingsFunction )
search_kwargs = {
"maximal_marginal_relevance": True,
"distance_metric": "cos",
"fetch_k": 100,
"k": 10,
}
retriever = vectordb.as_retriever(search_type="mmr", search_kwargs=search_kwargs)
chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
chain_type="stuff",
verbose=True,
max_tokens_limit=4096,
)
chain({"question": "ABC ABC ABC ABC", "chat_history":[]})```
### Suggestion:
_No response_ | Issue: Chroma DB | https://api.github.com/repos/langchain-ai/langchain/issues/5046/comments | 20 | 2023-05-20T20:35:07Z | 2024-07-29T09:33:13Z | https://github.com/langchain-ai/langchain/issues/5046 | 1,718,283,195 | 5,046 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
Hi All,
I'm developing a chatbot that run over PDF documents that stored on Pinecone.
I have an agent that use one tool (in my case it's `ConversationalRetrievalChain`) the chain works perfect when I'm asking any question.
But when I move forward and pass this chain as a tool I'm getting unexpected results.
For example when I asked "Who is Leo Messi?" (of course I don't have any info on him in my PDF file), I got real answer to the question "Leo Messi is Argentinian soccer player......"
I noticed that the agent search by himself answer to that question and don't run the tool I provided for him.
I have tried to edit the prompt of the agent that will not search data by himself, but without success.
Anyone face it too?
Thanks for help!
### Suggestion:
_No response_ | Agent answer questions that is not related to my custom data | https://api.github.com/repos/langchain-ai/langchain/issues/5044/comments | 6 | 2023-05-20T19:42:03Z | 2023-09-11T08:17:08Z | https://github.com/langchain-ai/langchain/issues/5044 | 1,718,266,994 | 5,044 |
[
"langchain-ai",
"langchain"
] | ### System Info
"langchain": "^0.0.75"
"@supabase/supabase-js": "^2.21.0"
### Who can help?
@hwchase17
### Information
- [x] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
export const query = async (query) => {
const model = new ChatOpenAI({
modelName: 'gpt-3.5-turbo',
});
const vectorStore = await SupabaseVectorStore.fromExistingIndex(
new OpenAIEmbeddings(),
{
client: SUPABASE_CLIENT,
tableName: 'documents',
queryName: 'match_documents_with_filters',
filter: { email: 'abc@gmail.com' }, //able to filter name with this as store in db.
}
);
const memory = new VectorStoreRetrieverMemory({
vectorStoreRetriever: vectorStore.asRetriever(5),
memoryKey: 'history',
});
await memory.saveContext(
{ input: 'My favorite sport is soccer' },
{ output: '...' }
);// this is not working when using metadata filtering.
const prompt =
PromptTemplate.fromTemplate(`The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Relevant pieces of conversation:
{history}
(You do not need to use these pieces of information if not relevant to your question)
Current conversation:
Human: {input}
AI:`);
const chain = new LLMChain({ llm: model, prompt: prompt, memory: memory });
const res2 = await chain.call({ input: query });
console.log({ res2 });
return res2;
};
query('what is my name? and what is my favorite sport?');
```
### Expected behavior
The name in the db is stored as abc.
I am able to get it by using the filter, but the favorite sport is missing in the answer.
output: "Your name is abc. As for your favorite sport, I don't have that information. Would you like me to look it up for you?"
expected output: "Your name is abc and your favorite sport is soccer."
able to get favorite sport as soccer if I don't use filtering but then the name is missing. | not able to use memory.saveContent and metadata filtering together. | https://api.github.com/repos/langchain-ai/langchain/issues/5043/comments | 2 | 2023-05-20T19:14:01Z | 2023-05-30T18:17:46Z | https://github.com/langchain-ai/langchain/issues/5043 | 1,718,260,661 | 5,043 |
[
"langchain-ai",
"langchain"
] | ### System Info
I have tried to run the AzureOpenAI example here for embedding: https://python.langchain.com/en/latest/modules/models/text_embedding/examples/azureopenai.html
Doing so, I get the following error:
`InvalidRequestError: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.embedding.Embedding'>`
I am using the current versions:
```
openai==0.27.7
langchain==0.0.174
```
I have even followed the issue described in #1560 to no avail
### Who can help?
_No response_
### 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/azureopenai.html
### Expected behavior
No errors | AzureOpenAI example error (embeddding) | https://api.github.com/repos/langchain-ai/langchain/issues/5042/comments | 2 | 2023-05-20T18:11:22Z | 2023-05-20T20:27:10Z | https://github.com/langchain-ai/langchain/issues/5042 | 1,718,245,431 | 5,042 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I am trying to access dolly-v2-3b using Langchain and HuggingFaceHub using the tutorial provided on the [website](https://python.langchain.com/en/latest/modules/models/llms/integrations/huggingface_hub.html).
However I am facing a few issues.
1. The instruction to `!pip install huggingface_hub > /dev/null` does not work. The error output is: The system cannot find the path specified.
2. Second, when using the example code to access dolly-v2-3b from Hugging Face Hub, the following code only provides a single word as output.
The code is shown below:
from langchain import HuggingFaceHub
repo_id = "databricks/dolly-v2-3b"
llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0, "max_length":64})
I have also tried changing the max_length and temperature, but this makes no difference.
### Suggestion:
_No response_ | Issue: Accessing Dolly-v2-3b via HuggingFaceHub and langchain | https://api.github.com/repos/langchain-ai/langchain/issues/5040/comments | 2 | 2023-05-20T16:05:33Z | 2023-09-15T16:12:11Z | https://github.com/langchain-ai/langchain/issues/5040 | 1,718,214,309 | 5,040 |
[
"langchain-ai",
"langchain"
] | ### Issue with current documentation:
Within the documentation, in the last sentence change should be **charge**.
Reference link: https://python.langchain.com/en/latest/modules/agents.html
<img width="511" alt="image" src="https://github.com/hwchase17/langchain/assets/67931050/52f6eacd-7930-451f-abd7-05eca9479390">
### Idea or request for content:
I propose correcting the misspelling as in change does not make sense and that the Action Agent is supposed to be in charge of the execution. | DOC: Misspelling in agents.rst | https://api.github.com/repos/langchain-ai/langchain/issues/5039/comments | 0 | 2023-05-20T15:50:20Z | 2023-06-01T01:06:04Z | https://github.com/langchain-ai/langchain/issues/5039 | 1,718,210,139 | 5,039 |
[
"langchain-ai",
"langchain"
] | ### System Info
MacOS, Python 3.11.2, langchain 0.0.174
### Who can help?
@vowelparrot
### Information
- [X] My own modified scripts
### Related Components
- [X] Tools / Toolkits
### Reproduction
I hit a strange error when using PythonREPL in an agent. It happens with this **recursive** Python function. It was written by gpt-3.5-turbo and is valid Python:
```python
def fibonacci(n):
if n <= 1:
return n
else:
return fibonacci(n-1) + fibonacci(n-2) # <-- this line will trigger the NameError
print(fibonacci(10))
```
Note that everything works as expected for non-recursive functions.
Using this function as string input, I reduced the issue to this minimal example:
```python
from langchain.utilities import PythonREPL
python_repl = PythonREPL()
cmd = "def fibonacci(n):\n if n <= 1:\n return n\n else:\n return fibonacci(n-1) + fibonacci(n-2)\nprint(fibonacci(10))"
python_repl.run(cmd)
```
> $ python t.py
> NameError("name 'fibonacci' is not defined")
When executed with `exec(cmd)`, it runs as expected. I found that `PythonREPL` runs the command with `exec(command, self.globals, self.locals)`.
Setting globals & locals in this `python_repl` instance makes the fragment work as expected:
```python
# [... as before ...]
python_repl.globals = globals()
python_repl.locals = locals()
python_repl.run(cmd)
```
This hack solves it only in the context of this simple example, but not if `python_repl` added as a tool to an `AgentExecutor`.
At the core, the issue seems to be caused by Python scopes, but my knowledge of this is not profound enough to fully understand what's happening.
### Expected behavior
I would have expected `PythonREPL` to accept recursive functions. | Reproducible NameError with recursive function in PythonREPL() | https://api.github.com/repos/langchain-ai/langchain/issues/5035/comments | 2 | 2023-05-20T14:08:20Z | 2024-06-07T08:08:18Z | https://github.com/langchain-ai/langchain/issues/5035 | 1,718,183,052 | 5,035 |
[
"langchain-ai",
"langchain"
] | ### Feature request
Have we considered the ability for an agent to spawn and manage other agents? It would be nice to be able to continue chatting with the main agent while other agents execute queries in the background.
Is this possible today? If so, what class should be used? If not, have we thought about how we might do it?
### Motivation
The ability for an agent to spawn and manage other agents.
### Your contribution
I've got some ideas on how we might manage this using asyncio and an event loop but don't want to jump the gun if we've already discussed it. | Spawning and managing agents | https://api.github.com/repos/langchain-ai/langchain/issues/5033/comments | 3 | 2023-05-20T12:50:55Z | 2023-09-10T16:14:41Z | https://github.com/langchain-ai/langchain/issues/5033 | 1,718,162,981 | 5,033 |
[
"langchain-ai",
"langchain"
] | ### Feature request
We want to request the addition of OAuth 2.0 support to the langchain package. OAuth 2.0 is a widely adopted industry-standard protocol for authorization. It would be immensely beneficial if langchain could incorporate this feature into its framework.
### Motivation
Our organization and many others are looking to use langchain for various internal projects. OAuth 2.0 support would significantly streamline integrating these projects with other systems in our ecosystem, which already use OAuth 2.0 for secure authorization.
Furthermore, langchain, a leading framework for large language model development, would significantly increase its appeal and utility by supporting OAuth 2.0. It would allow for more secure and efficient development, paramount in today's data-centric world.
This feature would not only benefit our organization but would also be advantageous for the wider langchain community. Furthermore, the addition of OAuth 2.0 support could potentially expand the usage and applicability of the langchain package across various domains and organizations.
### Your contribution
I can provide feedback on industry needs. | OAuth 2.0 Support | https://api.github.com/repos/langchain-ai/langchain/issues/5032/comments | 3 | 2023-05-20T12:19:52Z | 2023-09-19T16:09:50Z | https://github.com/langchain-ai/langchain/issues/5032 | 1,718,155,380 | 5,032 |
[
"langchain-ai",
"langchain"
] | ### System Info
aiohttp==3.8.4
aiosignal==1.3.1
aniso8601==9.0.1
anyio==3.6.2
async-timeout==4.0.2
attrs==23.1.0
backoff==2.2.1
blinker==1.6.2
certifi==2023.5.7
charset-normalizer==3.1.0
chromadb==0.3.23
click==8.1.3
clickhouse-connect==0.5.24
colorama==0.4.6
dataclasses-json==0.5.7
duckdb==0.8.0
environs==9.5.0
fastapi==0.95.2
filelock==3.12.0
Flask==2.3.2
Flask-RESTful==0.3.9
frozenlist==1.3.3
fsspec==2023.5.0
greenlet==2.0.2
grpcio==1.53.0
h11==0.14.0
hnswlib==0.7.0
httptools==0.5.0
huggingface-hub==0.14.1
idna==3.4
itsdangerous==2.1.2
Jinja2==3.1.2
joblib==1.2.0
langchain==0.0.174
lz4==4.3.2
MarkupSafe==2.1.2
marshmallow==3.19.0
marshmallow-enum==1.5.1
monotonic==1.6
mpmath==1.3.0
multidict==6.0.4
mypy-extensions==1.0.0
networkx==3.1
nltk==3.8.1
numexpr==2.8.4
numpy==1.24.3
openai==0.27.6
openapi-schema-pydantic==1.2.4
packaging==23.1
pandas==2.0.1
Pillow==9.5.0
posthog==3.0.1
protobuf==4.23.1
pydantic==1.10.7
pymilvus==2.2.8
python-dateutil==2.8.2
python-dotenv==1.0.0
pytz==2023.3
PyYAML==6.0
regex==2023.5.5
requests==2.30.0
scikit-learn==1.2.2
scipy==1.10.1
sentence-transformers==2.2.2
sentencepiece==0.1.99
six==1.16.0
sniffio==1.3.0
SQLAlchemy==2.0.13
starlette==0.27.0
sympy==1.12
tenacity==8.2.2
threadpoolctl==3.1.0
tiktoken==0.4.0
tokenizers==0.13.3
torch==2.0.1
torchvision==0.15.2
tqdm==4.65.0
transformers==4.29.2
typing-inspect==0.8.0
typing_extensions==4.5.0
tzdata==2023.3
ujson==5.7.0
urllib3==2.0.2
uvicorn==0.22.0
watchfiles==0.19.0
websockets==11.0.3
Werkzeug==2.3.4
yarl==1.9.2
zstandard==0.21.0
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Reproduction Steps:
1. Create an instance of the Collection class.
2. Initialize the LangChain object and make sure it is properly configured.
3. Create a Document object and set its page_content and metadata attributes appropriately.
4. Call the update_document method on the LangChain object, passing the document_id and document as arguments.
Expected Result:
The update_document method should update the specified document in the collection with the provided text and metadata.
Actual Result:
An AttributeError is raised with the following traceback:
```plain
File "E:\AI Projects\flask-backend\venv\lib\site-packages\langchain\vectorstores\chroma.py", line 351, in update_document
self._collection.update_document(document_id, text, metadata)
AttributeError: 'Collection' object has no attribute 'update_document'
```
Note that the error occurs in the chroma.py file, specifically in the update_document method of the Collection class.
### Expected behavior
The update_document method should update the specified document in the collection with the provided text and metadata.
| AttributeError: 'Collection' object has no attribute 'update_document' | https://api.github.com/repos/langchain-ai/langchain/issues/5031/comments | 4 | 2023-05-20T12:01:57Z | 2023-09-15T22:13:00Z | https://github.com/langchain-ai/langchain/issues/5031 | 1,718,150,791 | 5,031 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
INFO: Will watch for changes in these directories: ['/Users/abdibrokhim/VisualStudioCode/draft/antrophicCloudeLangchainTutorialApp']
INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO: Started reloader process [58140] using WatchFiles
Process SpawnProcess-1:
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/uvicorn/_subprocess.py", line 76, in subprocess_started
target(sockets=sockets)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/uvicorn/server.py", line 60, in run
return asyncio.run(self.serve(sockets=sockets))
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "uvloop/loop.pyx", line 1517, in uvloop.loop.Loop.run_until_complete
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/uvicorn/server.py", line 67, in serve
config.load()
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/uvicorn/config.py", line 477, in load
self.loaded_app = import_from_string(self.app)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/uvicorn/importer.py", line 24, in import_from_string
raise exc from None
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/uvicorn/importer.py", line 21, in import_from_string
module = importlib.import_module(module_str)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 883, in exec_module
File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
File "/Users/abdibrokhim/VisualStudioCode/draft/antrophicCloudeLangchainTutorialApp/./main.py", line 6, in <module>
from langchain import PromptTemplate, LLMChain
ModuleNotFoundError: No module named 'langchain'
### Suggestion:
_No response_ | Help me please, even i installed langchain library it says "No module named 'langchain'" why? | https://api.github.com/repos/langchain-ai/langchain/issues/5028/comments | 3 | 2023-05-20T10:55:13Z | 2023-05-20T19:24:56Z | https://github.com/langchain-ai/langchain/issues/5028 | 1,718,135,190 | 5,028 |
[
"langchain-ai",
"langchain"
] | ### System Info
LangChain version = 0.0.167
Python version = 3.11.0
System = Windows 11 (using Jupyter)
### Who can help?
- @hwchase17
- @agola11
- @UmerHA (I have a fix ready, will submit a PR)
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
import os
os.environ["OPENAI_API_KEY"] = "..."
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.prompts.chat import ChatPromptTemplate
from langchain.schema import messages_from_dict
role_strings = [
("system", "you are a bird expert"),
("human", "which bird has a point beak?")
]
prompt = ChatPromptTemplate.from_role_strings(role_strings)
chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)
chain.run({})
```
### Expected behavior
Chain should run | ChatOpenAI models don't work with prompts created via ChatPromptTemplate.from_role_strings | https://api.github.com/repos/langchain-ai/langchain/issues/5027/comments | 1 | 2023-05-20T10:39:18Z | 2023-05-30T20:56:20Z | https://github.com/langchain-ai/langchain/issues/5027 | 1,718,131,410 | 5,027 |
[
"langchain-ai",
"langchain"
] | ### Feature request
A utility to cleanup documents or texts after loading into langchain document formats. The LLMs sometime even consider redundant whitespaces in the context as tokens leading to wastage of tokens. What I'm proposing is an utility that binds with the existing adapters in langchain and/or llama-index that cleans up documents (getting read of whitespaces, getting rid of special characters, restructuring it in a readable, yet cost efficient format etc.) all while keeping the LLMs readability of the document in mind. The user should be able to choose what kind of a cleanup they want.
### Motivation
I was having a trouble with whitespaces in the project that I'm building with langchain where it lead to high consumption of tokens. I tried to find some utility either in langchain or in llama-index that I could use right out of the box for this and was not able to find one.
### Your contribution
I could help build this feature, If I have some instructions (Dos and donts) | Document Cleanup Tools | https://api.github.com/repos/langchain-ai/langchain/issues/5026/comments | 1 | 2023-05-20T10:36:52Z | 2023-09-19T16:09:56Z | https://github.com/langchain-ai/langchain/issues/5026 | 1,718,130,806 | 5,026 |
[
"langchain-ai",
"langchain"
] | ### System Info
I am using version `0.0.171` of Langchain.
Running a mac, M1, 2021, OS Ventura. Can do most all of Langchain operations without errors.
Except for this issue. Installed through pyenv, python 3.11.
```requirements.txt
aiohttp==3.8.4
aiosignal==1.3.1
anyio==3.6.2
appnope==0.1.3
argilla==1.7.0
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens==2.2.1
async-timeout==4.0.2
attrs==23.1.0
backcall==0.2.0
backoff==2.2.1
beautifulsoup4==4.12.2
-e git+ssh://git@github.com/mad-start/big-macs-llm.git@2998ca685b68d74ef20a12fe74c0f4cab6e48dcb#egg=big_macs_llm
bleach==6.0.0
certifi==2023.5.7
cffi==1.15.1
charset-normalizer==3.1.0
click==8.1.3
comm==0.1.3
commonmark==0.9.1
contourpy==1.0.7
cryptography==40.0.2
cycler==0.11.0
dataclasses-json==0.5.7
datasets==2.12.0
debugpy==1.6.7
decorator==5.1.1
defusedxml==0.7.1
Deprecated==1.2.13
dill==0.3.6
einops==0.6.1
et-xmlfile==1.1.0
executing==1.2.0
fastjsonschema==2.16.3
filelock==3.12.0
fonttools==4.39.4
fqdn==1.5.1
frozenlist==1.3.3
fsspec==2023.5.0
h11==0.14.0
httpcore==0.16.3
httpx==0.23.3
huggingface-hub==0.14.1
idna==3.4
iniconfig==2.0.0
ipykernel==6.23.1
ipython==8.13.2
ipython-genutils==0.2.0
ipywidgets==8.0.6
isoduration==20.11.0
jedi==0.18.2
Jinja2==3.1.2
joblib==1.2.0
jsonpointer==2.3
jsonschema==4.17.3
jupyter==1.0.0
jupyter-console==6.6.3
jupyter-events==0.6.3
jupyter_client==8.2.0
jupyter_core==5.3.0
jupyter_server==2.5.0
jupyter_server_terminals==0.4.4
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.7
kiwisolver==1.4.4
langchain==0.0.171
lxml==4.9.2
Markdown==3.4.3
MarkupSafe==2.1.2
marshmallow==3.19.0
marshmallow-enum==1.5.1
matplotlib==3.7.1
matplotlib-inline==0.1.6
mistune==2.0.5
monotonic==1.6
mpmath==1.3.0
msg-parser==1.2.0
multidict==6.0.4
multiprocess==0.70.14
mypy-extensions==1.0.0
nbclassic==1.0.0
nbclient==0.7.4
nbconvert==7.4.0
nbformat==5.8.0
nest-asyncio==1.5.6
networkx==3.1
nltk==3.8.1
notebook==6.5.4
notebook_shim==0.2.3
numexpr==2.8.4
numpy==1.23.5
olefile==0.46
openai==0.27.6
openapi-schema-pydantic==1.2.4
openpyxl==3.1.2
packaging==23.1
pandas==1.5.3
pandocfilters==1.5.0
parso==0.8.3
pdf2image==1.16.3
pdfminer.six==20221105
pexpect==4.8.0
pickleshare==0.7.5
Pillow==9.5.0
platformdirs==3.5.1
pluggy==1.0.0
prometheus-client==0.16.0
prompt-toolkit==3.0.38
psutil==5.9.5
ptyprocess==0.7.0
pure-eval==0.2.2
pyarrow==12.0.0
pycparser==2.21
pydantic==1.10.7
Pygments==2.15.1
pypandoc==1.11
pyparsing==3.0.9
pyrsistent==0.19.3
pytest==7.3.1
python-dateutil==2.8.2
python-docx==0.8.11
python-dotenv==1.0.0
python-json-logger==2.0.7
python-magic==0.4.27
python-pptx==0.6.21
pytz==2023.3
PyYAML==6.0
pyzmq==25.0.2
qtconsole==5.4.3
QtPy==2.3.1
regex==2023.5.5
requests==2.30.0
responses==0.18.0
rfc3339-validator==0.1.4
rfc3986==1.5.0
rfc3986-validator==0.1.1
rich==13.0.1
scikit-learn==1.2.2
scipy==1.10.1
Send2Trash==1.8.2
sentence-transformers==2.2.2
sentencepiece==0.1.99
six==1.16.0
sniffio==1.3.0
soupsieve==2.4.1
SQLAlchemy==2.0.13
stack-data==0.6.2
sympy==1.12
tabulate==0.9.0
tenacity==8.2.2
terminado==0.17.1
text-generation==0.5.2
threadpoolctl==3.1.0
tiktoken==0.4.0
tinycss2==1.2.1
tokenizers==0.13.3
torch==2.0.1
torchvision==0.15.2
tornado==6.3.2
tqdm==4.65.0
traitlets==5.9.0
transformers==4.29.2
typer==0.9.0
typing-inspect==0.8.0
typing_extensions==4.5.0
tzdata==2023.3
unstructured==0.6.8
uri-template==1.2.0
urllib3==2.0.2
wcwidth==0.2.6
webcolors==1.13
webencodings==0.5.1
websocket-client==1.5.1
widgetsnbextension==4.0.7
wrapt==1.14.1
XlsxWriter==3.1.0
xxhash==3.2.0
yarl==1.9.2
```
### Who can help?
@eyurtsev Thank you:
I got this code from [Langchain instructions here](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/file_directory.html#change-loader-class). While I am able to load and split a python file one at a time, I cannot do so for DirectoryLoaders that have `*.py` in the glob pattern. I tested this out without langchain and it worked just fine.
```python
from langchain.document_loaders.text import TextLoader
from langchain.document_loaders.directory import DirectoryLoader
loader = DirectoryLoader('../../../src', glob="**/*.py", loader_cls=TextLoader)
directory_loader.load()
```
and
```python
from langchain.document_loaders.directory import DirectoryLoader
from langchain.document_loaders import PythonLoader
loader = DirectoryLoader('../../../../../', glob="**/*.py", loader_cls=PythonLoader)
directory_loader.load()
```
yields an error:
`ValueError: Invalid file ../../../src/my_library/__init__.py. The FileType.UNK file type is not supported in partition.`
I looked up this error on the unstructured issues page and then I ran the following code with unstructured and it didn't error out and displayed the contents of the python module.
```python
from unstructured.partition.text import partition_text
elements = partition_text(filename='setup.py')
print("\n\n".join([str(el) for el in elements]))
```
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have written more detail above, but this can be reproduced like this.
```python
from langchain.document_loaders.directory import DirectoryLoader
from langchain.document_loaders import PythonLoader
loader = DirectoryLoader('../../../../../', glob="**/*.py", loader_cls=PythonLoader)
directory_loader.load()
```
### Expected behavior
```zsh
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[73], line 6
2 from langchain.document_loaders import PythonLoader
5 loader = DirectoryLoader('../../../../../', glob="**/*.py", loader_cls=PythonLoader)
----> 6 directory_loader.load()
File ~/.pyenv/versions/3.11.3/envs/big-macs-llm/lib/python3.11/site-packages/langchain/document_loaders/directory.py:103, in DirectoryLoader.load(self)
101 else:
102 for i in items:
--> 103 self.load_file(i, p, docs, pbar)
105 if pbar:
106 pbar.close()
File ~/.pyenv/versions/3.11.3/envs/big-macs-llm/lib/python3.11/site-packages/langchain/document_loaders/directory.py:69, in DirectoryLoader.load_file(self, item, path, docs, pbar)
67 logger.warning(e)
68 else:
---> 69 raise e
70 finally:
71 if pbar:
File ~/.pyenv/versions/3.11.3/envs/big-macs-llm/lib/python3.11/site-packages/langchain/document_loaders/directory.py:63, in DirectoryLoader.load_file(self, item, path, docs, pbar)
61 if _is_visible(item.relative_to(path)) or self.load_hidden:
62 try:
---> 63 sub_docs = self.loader_cls(str(item), **self.loader_kwargs).load()
64 docs.extend(sub_docs)
65 except Exception as e:
File ~/.pyenv/versions/3.11.3/envs/big-macs-llm/lib/python3.11/site-packages/langchain/document_loaders/unstructured.py:70, in UnstructuredBaseLoader.load(self)
68 def load(self) -> List[Document]:
69 """Load file."""
---> 70 elements = self._get_elements()
71 if self.mode == "elements":
72 docs: List[Document] = list()
File ~/.pyenv/versions/3.11.3/envs/big-macs-llm/lib/python3.11/site-packages/langchain/document_loaders/unstructured.py:104, in UnstructuredFileLoader._get_elements(self)
101 def _get_elements(self) -> List:
102 from unstructured.partition.auto import partition
--> 104 return partition(filename=self.file_path, **self.unstructured_kwargs)
File ~/.pyenv/versions/3.11.3/envs/big-macs-llm/lib/python3.11/site-packages/unstructured/partition/auto.py:206, in partition(filename, content_type, file, file_filename, url, include_page_breaks, strategy, encoding, paragraph_grouper, headers, ssl_verify, ocr_languages, pdf_infer_table_structure, xml_keep_tags)
204 else:
205 msg = "Invalid file" if not filename else f"Invalid file {filename}"
--> 206 raise ValueError(f"{msg}. The {filetype} file type is not supported in partition.")
208 for element in elements:
209 element.metadata.url = url
ValueError: Invalid file ../../../src/__init__.py. The FileType.UNK file type is not supported in partition.
``` | Cannot load python files for Directory Loader | https://api.github.com/repos/langchain-ai/langchain/issues/5025/comments | 1 | 2023-05-20T09:48:56Z | 2023-09-10T16:14:45Z | https://github.com/langchain-ai/langchain/issues/5025 | 1,718,119,181 | 5,025 |
[
"langchain-ai",
"langchain"
] | ### System Info
Langchain version: 0.0.173
numpy version: 1.24.3
### Related Components
- [X] Embedding Models
### Reproduction
```python
from sentence_transformers import SentenceTransformer
import numpy as np
from langchain.embeddings import HuggingFaceEmbeddings
t = 'langchain embedding'
m = HuggingFaceEmbeddings(encode_kwargs={"normalize_embeddings": True})
# SentenceTransformer embeddings with unit norm
x = SentenceTransformer(m.model_name).encode(t, normalize_embeddings=True)
# Langchain.Huggingface embeddings with unit norm
y = m.embed_query(t)
print(f'L2 norm of SentenceTransformer: {np.linalg.norm(x)}. \nL2 norm of Langchain.Huggingface: {np.linalg.norm(y)}')
```
### Expected behavior
Both of these two L2 norm results shoud be 1. But I got as blow:
```
L2 norm of SentenceTransformer: 1.0.
L2 norm of Langchain.Huggingface: 1.0000000445724682
```
I think the problem came from this [code](https://github.com/hwchase17/langchain/blob/27e63b977aa07cb4ccb25b006c9af17310a9f530/langchain/embeddings/huggingface.py#LL87C34-L87C34). When converting array to list, the numbers got bigger | Precision of HuggingFaceEmbeddings.embed_query changes | https://api.github.com/repos/langchain-ai/langchain/issues/5024/comments | 2 | 2023-05-20T08:44:51Z | 2023-09-10T16:14:51Z | https://github.com/langchain-ai/langchain/issues/5024 | 1,718,103,064 | 5,024 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I am trying to query databases with multiple tables. Because of DB metadata, the prompt length exceeds the max token limit of 4000 of OpenAI . Do you have any ideas on how to solve this problem for databases with medium to large metadata? I have tried it for Postgrsql and Presto.
`InvalidRequestError: This model's maximum context length is 4097 tokens, however you requested 4995 tokens (4739 in your prompt; 256 for the completion). Please reduce your prompt; or completion length.`
### Suggestion:
_No response_ | SQL database with metadata exceeding 4000 token limit of Open AI. | https://api.github.com/repos/langchain-ai/langchain/issues/5023/comments | 1 | 2023-05-20T08:26:04Z | 2023-05-20T08:30:26Z | https://github.com/langchain-ai/langchain/issues/5023 | 1,718,098,699 | 5,023 |
[
"langchain-ai",
"langchain"
] | ### Feature request
i have using RWKV llm with langchian and everything is ok,
but i don't know how to rewrite `_call` for add lora for my own RWKV Model, can you give some tips
### Motivation
-
### Your contribution
- | How to add lora for RWKV in using langchian | https://api.github.com/repos/langchain-ai/langchain/issues/5022/comments | 1 | 2023-05-20T08:12:51Z | 2023-09-10T16:14:57Z | https://github.com/langchain-ai/langchain/issues/5022 | 1,718,095,658 | 5,022 |
[
"langchain-ai",
"langchain"
] |
I was trying to use `gpt-3.5` family for models as a proxy for codex to generate code.
I tried using `PromptTemplate` and `PydanticOutputParser` parser to get code output. But turns out the LLM mixes up the Prompt template example in the response
**Parser**
```python
class CodeCompletion(BaseModel):
code: str = Field(description="completed code")
parser = PydanticOutputParser(pydantic_object=CodeCompletion)
```
**Prompt Template**
```python
template = """
I want you to act as an Code Assistant bot. please complete the following code written in {programming_language} programming language
{code}
Format the response in {format_instructions}
"""
code_completion_prompt = PromptTemplate(
input_variables=["programming_language", "code"],
partial_variables={"format_instructions": parser.get_format_instructions()},
template=template,
)
```
**Prompt (generated by prompt template)**
```
I want you to act as an Code Assistant bot. please complete the following code written in python programming language
# Write a function to read json data from s3
def read_from_s3():
pass
Format the response in The output should be formatted as a JSON instance that conforms to the JSON schema below.
As an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}}
the object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted.
Here is the output schema:
\```
{"properties": {"code": {"title": "Code", "description": "completed code", "type": "string"}}, "required": ["code"]}
\```
```
**Output from LLM**
``` File "/Users/avikant/Projects/HackerGPT-backend/venv/lib/python3.11/site-packages/langchain/output_parsers/pydantic.py", line 31, in parse
raise OutputParserException(msg)
langchain.schema.OutputParserException: Failed to parse CodeCompletion from completion # Write a function to read json data from s3
import json
def read_from_s3(file_path):
with open(file_path, 'r') as f:
data = json.load(f)
return {"code": data}
response = read_from_s3("s3://path/to/file.json")
print(json.dumps(response)) # outputs {"code": {<data from file.json>}} in JSON format.. Got: Expecting value: line 1 column 10 (char 9)
```
### Suggestion:
A way to parse the code generation output | Issue: Prompt template does not work with Code related queries | https://api.github.com/repos/langchain-ai/langchain/issues/5020/comments | 2 | 2023-05-20T07:22:06Z | 2023-09-15T22:12:59Z | https://github.com/langchain-ai/langchain/issues/5020 | 1,718,084,155 | 5,020 |
[
"langchain-ai",
"langchain"
] | ### System Info
Python Version = 3.10
Langchain Version = 0.0.174
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
Good evening,
I have been working on my own custom wrapper for a Huggingface model. I am using a custom wrapper as a plan to add additional features such as LoRA finetuning. I have loaded the model in 8 bit and received the error "topk_cpu" not implemented for 'Half'. This led me to conclude 8 bit models are currently not supported for inference inside Langchain, though I am unsure if this is due to the library or faulty code on my end.
The code for the model is as follows:
#Langchain Imports
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
#Huggingface Inputs
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import torch
import torch.nn as nn
import bitsandbytes as bnb
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
#Pydantic imports
from pydantic import BaseModel, Field
class HuggingFaceLLM(LLM):
model_name: str = Field(default="facebook/opt-6.7b")
tokenizer: AutoTokenizer = Field(default=None)
model: AutoModelForCausalLM = Field(default=None)
device: torch.device = Field(default=None)
def __init__(self, model_name="facebook/opt-6.7b"):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True, device_map='auto')
#self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#self.model.to(self.device)
@property
def _llm_type(self) -> str:
return "huggingface"
def _call(
self,
prompt: str,
temperature: float = 1.0,
max_tokens: int = 512,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
if stop is not None:
raise ValueError("stop kwargs are not permitted.")
# prepare input data
input_ids = self.tokenizer.encode(prompt, return_tensors='pt')
# generate text
gen_tokens = self.model.generate(
input_ids,
do_sample=True,
temperature=temperature,
max_length=max_tokens,
)
# decode generated text
gen_text = self.tokenizer.decode(gen_tokens[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
return gen_text
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"model_name": self.model.config._name_or_path}
#Build model
llm = HuggingFaceLLM("facebook/opt-6.7b")
#Inference
llm("What is the fastest car")
---------------------------------------------------------------------------------------------------------------------------------------------
The traceback is here as follows:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <module>:1 │
│ │
│ ❱ 1 llm("What is the fastest car") │
│ 2 │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/langchain/llms/base │
│ .py:291 in __call__ │
│ │
│ 288 │ ) -> str: │
│ 289 │ │ """Check Cache and run the LLM on the given prompt and input.""" │
│ 290 │ │ return ( │
│ ❱ 291 │ │ │ self.generate([prompt], stop=stop, callbacks=callbacks) │
│ 292 │ │ │ .generations[0][0] │
│ 293 │ │ │ .text │
│ 294 │ │ ) │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/langchain/llms/base │
│ .py:191 in generate │
│ │
│ 188 │ │ │ │ ) │
│ 189 │ │ │ except (KeyboardInterrupt, Exception) as e: │
│ 190 │ │ │ │ run_manager.on_llm_error(e) │
│ ❱ 191 │ │ │ │ raise e │
│ 192 │ │ │ run_manager.on_llm_end(output) │
│ 193 │ │ │ return output │
│ 194 │ │ if len(missing_prompts) > 0: │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/langchain/llms/base │
│ .py:185 in generate │
│ │
│ 182 │ │ │ ) │
│ 183 │ │ │ try: │
│ 184 │ │ │ │ output = ( │
│ ❱ 185 │ │ │ │ │ self._generate(prompts, stop=stop, run_manager=run_manager) │
│ 186 │ │ │ │ │ if new_arg_supported │
│ 187 │ │ │ │ │ else self._generate(prompts, stop=stop) │
│ 188 │ │ │ │ ) │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/langchain/llms/base │
│ .py:405 in _generate │
│ │
│ 402 │ │ new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") │
│ 403 │ │ for prompt in prompts: │
│ 404 │ │ │ text = ( │
│ ❱ 405 │ │ │ │ self._call(prompt, stop=stop, run_manager=run_manager) │
│ 406 │ │ │ │ if new_arg_supported │
│ 407 │ │ │ │ else self._call(prompt, stop=stop) │
│ 408 │ │ │ ) │
│ │
│ in _call:33 │
│ │
│ 30 │ │ input_ids = self.tokenizer.encode(prompt, return_tensors='pt') │
│ 31 │ │ │
│ 32 │ │ # generate text │
│ ❱ 33 │ │ gen_tokens = self.model.generate( │
│ 34 │ │ │ input_ids, │
│ 35 │ │ │ do_sample=True, │
│ 36 │ │ │ temperature=temperature, │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/torch/utils/_contex │
│ tlib.py:115 in decorate_context │
│ │
│ 112 │ @functools.wraps(func) │
│ 113 │ def decorate_context(*args, **kwargs): │
│ 114 │ │ with ctx_factory(): │
│ ❱ 115 │ │ │ return func(*args, **kwargs) │
│ 116 │ │
│ 117 │ return decorate_context │
│ 118 │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/transformers/genera │
│ tion/utils.py:1565 in generate │
│ │
│ 1562 │ │ │ ) │
│ 1563 │ │ │ │
│ 1564 │ │ │ # 13. run sample │
│ ❱ 1565 │ │ │ return self.sample( │
│ 1566 │ │ │ │ input_ids, │
│ 1567 │ │ │ │ logits_processor=logits_processor, │
│ 1568 │ │ │ │ logits_warper=logits_warper, │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/transformers/genera │
│ tion/utils.py:2626 in sample │
│ │
│ 2623 │ │ │ │
│ 2624 │ │ │ # pre-process distribution │
│ 2625 │ │ │ next_token_scores = logits_processor(input_ids, next_token_logits) │
│ ❱ 2626 │ │ │ next_token_scores = logits_warper(input_ids, next_token_scores) │
│ 2627 │ │ │ │
│ 2628 │ │ │ # Store scores, attentions and hidden_states when required │
│ 2629 │ │ │ if return_dict_in_generate: │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/transformers/genera │
│ tion/logits_process.py:92 in __call__ │
│ │
│ 89 │ │ │ │ │ ) │
│ 90 │ │ │ │ scores = processor(input_ids, scores, **kwargs) │
│ 91 │ │ │ else: │
│ ❱ 92 │ │ │ │ scores = processor(input_ids, scores) │
│ 93 │ │ return scores │
│ 94 │
│ 95 │
│ │
│ /home/username/anaconda3/envs/langchainenv/lib/python3.10/site-packages/transformers/genera │
│ tion/logits_process.py:302 in __call__ │
│ │
│ 299 │ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch. │
│ 300 │ │ top_k = min(self.top_k, scores.size(-1)) # Safety check │
│ 301 │ │ # Remove all tokens with a probability less than the last token of the top-k │
│ ❱ 302 │ │ indices_to_remove = scores < torch.topk(scores, top_k)[0][..., -1, None] │
│ 303 │ │ scores = scores.masked_fill(indices_to_remove, self.filter_value) │
│ 304 │ │ return scores │
│ 305 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: "topk_cpu" not implemented for 'Half'
### Expected behavior
As mentioned earlier, I am not sure if 8 bit precision is supported for inference inside Langchain.
I would expect the inference to run as expected. When the model was loaded in full 16 bit precision the inference worked successfully.
| Text Generation on an 8 bit model. | https://api.github.com/repos/langchain-ai/langchain/issues/5019/comments | 2 | 2023-05-20T05:49:32Z | 2023-05-21T20:58:06Z | https://github.com/langchain-ai/langchain/issues/5019 | 1,718,062,726 | 5,019 |
[
"langchain-ai",
"langchain"
] | ### RetrievalQA chain with GPT4All takes an extremely long time to run (doesn't end)
I encounter massive runtimes when [running a RetrievalQA chain](https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html) with a locally downloaded GPT4All LLM. Unsure what's causing this.
I pass a GPT4All model (loading [ggml-gpt4all-j-v1.3-groovy.bin](https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin) model that I downloaded locally) to the RetrievalQA Chain. I have one source text document and use sentence-transformers from HuggingFace for embeddings (I'm using a fairly small model: [all-MiniLM-L6-v2](https://www.sbert.net/docs/pretrained_models.html)).
```python
llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=callbacks, verbose=True)
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=docsearch.as_retriever())
query = "How did the pandemic affect businesses"
ans = qa.run(query)
```
For some reason, the running the chain on a query takes an extremely long time to run (>25 minutes).
Is this due to hardware limitations or something else? I'm able to run queries directly against the GPT4All model I downloaded locally fairly quickly (like the example shown [here](https://docs.gpt4all.io/gpt4all_python.html)), which is why I'm unclear on what's causing this massive runtime.
Hardware: M1 Mac, macOS 12.1, 8 GB RAM, Python 3.10.11
### Suggestion:
_No response_ | Issue: Very long runtimes for RetrievalQA chain with GPT4All | https://api.github.com/repos/langchain-ai/langchain/issues/5016/comments | 8 | 2023-05-20T03:57:58Z | 2024-01-06T03:53:34Z | https://github.com/langchain-ai/langchain/issues/5016 | 1,718,038,199 | 5,016 |
[
"langchain-ai",
"langchain"
] | ### Feature request
None of the search tools have async support.
### Motivation
Async calls are being used langchain setup with FastAPI. Not having async support for these tools blocks there use.
### Your contribution
Happy to help out where needed. | Async Support | Search Tools | https://api.github.com/repos/langchain-ai/langchain/issues/5011/comments | 9 | 2023-05-20T01:37:04Z | 2024-06-01T00:07:30Z | https://github.com/langchain-ai/langchain/issues/5011 | 1,717,978,496 | 5,011 |
[
"langchain-ai",
"langchain"
] | ### Feature request
I have been playing around with the Plan & Execute Agent.
Would love to see async support implemented.
### Motivation
Would like to use it as a drop in agent replacement for my existing async setup.
### Your contribution
Happy to help out where needed. | Async Support | Plan & Execute | https://api.github.com/repos/langchain-ai/langchain/issues/5010/comments | 2 | 2023-05-20T01:34:11Z | 2023-09-12T16:14:29Z | https://github.com/langchain-ai/langchain/issues/5010 | 1,717,977,580 | 5,010 |
[
"langchain-ai",
"langchain"
] | ### System Info
Broken by #4915
Error: `Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.embedding.Embedding'>`
I'm putting a PR out to fix this now.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run example notebook: https://github.com/hwchase17/langchain/blob/22d844dc0795e7e53a4cc499bf4974cb83df490d/docs/modules/models/text_embedding/examples/azureopenai.ipynb
### Expected behavior
Embedding using Azure OpenAI should work. | Azure OpenAI Embeddings failed due to no deployment_id set. | https://api.github.com/repos/langchain-ai/langchain/issues/5001/comments | 2 | 2023-05-19T20:18:47Z | 2023-05-22T06:43:51Z | https://github.com/langchain-ai/langchain/issues/5001 | 1,717,774,987 | 5,001 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain==0.0.169
openai==0.27.6
### Who can help?
@hwchase17
@agola11
@vowelparrot
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.embeddings import OpenAIEmbeddings
from dotenv import load_dotenv
load_dotenv('.env')
ChatOpenAI(temperature=0, max_tokens=500, model_name='gpt-3.5-turbo', openai_api_base = os.environ['OPENAI_API_BASE'] ).call_as_llm('Hi')
### Expected behavior
[nltk_data] Downloading package stopwords to /home/sahand/nltk_data...
[nltk_data] Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to /home/sahand/nltk_data...
[nltk_data] Package punkt is already up-to-date!
Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.chat_completion.ChatCompletion'>
Invalid API key.
| ChatOpenAI: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.chat_completion.ChatCompletion'> | https://api.github.com/repos/langchain-ai/langchain/issues/5000/comments | 19 | 2023-05-19T20:17:41Z | 2024-02-09T16:10:57Z | https://github.com/langchain-ai/langchain/issues/5000 | 1,717,773,530 | 5,000 |
[
"langchain-ai",
"langchain"
] | ### System Info
I tried using `code-davinci-002` model with LangChain, looks like it is not supported in the LLM wrapper'
```openai.error.InvalidRequestError: The model: `code-davinci-001` does not exist```
<img width="619" alt="image" src="https://github.com/hwchase17/langchain/assets/41926176/4b8bb7a7-ad4c-4618-a5f9-a8bc2a25705e">
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.llms import OpenAI
llm = OpenAI(model_name='code-davinci-002')
llm('write python code to create json object in s3')
```
### Expected behavior
The expected behaviour is that the model should load as an LLM object | code-davinci-002 does not work with LangChain | https://api.github.com/repos/langchain-ai/langchain/issues/4999/comments | 6 | 2023-05-19T20:17:35Z | 2024-01-11T12:31:46Z | https://github.com/langchain-ai/langchain/issues/4999 | 1,717,773,452 | 4,999 |
[
"langchain-ai",
"langchain"
] | ### System Info
```
from langchain.agents.agent_toolkits.openapi import planner
from langchain.llms import PromptLayerOpenAI
llm = PromptLayerOpenAI(model_name="gpt-4", temperature=0.25)
content_agent = planner.create_openapi_agent(api_spec=content_api_spec, requests_wrapper=Requests(headers = {"Authorization": f"Bearer {token}"}), llm=llm)
```
Expected that API requests are logged.
However, nothing is actually logged.
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
See code
### Expected behavior
Expected that API requests are logged on promptlayer.com.
However, nothing is actually logged. | PromptLayerOpenAI doesn't work with planner | https://api.github.com/repos/langchain-ai/langchain/issues/4995/comments | 1 | 2023-05-19T18:56:35Z | 2023-09-10T16:15:07Z | https://github.com/langchain-ai/langchain/issues/4995 | 1,717,682,308 | 4,995 |
[
"langchain-ai",
"langchain"
] | ### System Info
Python 3, Google Colab/Mac-OS Conda Env, Langchain v0.0.173, Dev2049/obsidian patch #4204
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
See Colab: https://colab.research.google.com/drive/1YSfKTQ92RZEJ-z-hlwHL7f5rQ-evTgx4?usp=sharing
1. Have Obsidian Files with Front_Matter other then simple key_values
2. Import ObsidianLoader and create loader with the Obsidian Vault
3. load documents
YAML frontmatter is parsed to:
metadata={'source': 'Demo-Note.md', 'path': 'Obsidian_Vault/Demo-Note.md', 'created': 1684513990.4561925, 'last_modified': 1684513964.4224126, 'last_accessed': 1684514007.4610083, 'normal': 'frontmatter is just key-value based', 'list_frontmatter': '', 'dictionary_frontmatter': '', 'can': 'contain', 'different': 'key-value-paris', 'and': '[even, nest, these, types]'}
### Expected behavior
YAML Frontmatter should be parsed according to YAML rules
---
normal: frontmatter is just key-value based
list_frontmatter:
- can
- be
- an array
dictionary_frontmatter:
can: contain
different: key-value-paris
and: [even, nest, these, types]
---
-->
```
{
'norma'l: 'frontmatter is just key-value based,
'list_frontmatter': ['can', 'be', 'an array'],
'dictionary_frontmatter' : {
'can' : 'contain',
'different': 'key-value-pairs',
'and' : ['even', 'nest', 'theses', 'types]
}
}
``` | ObsidianLoader doesn't Parse Frontmatter of Obsidian Files according to YAML, but line-by-line and only for key-value pairs | https://api.github.com/repos/langchain-ai/langchain/issues/4991/comments | 1 | 2023-05-19T16:45:59Z | 2023-09-10T16:15:11Z | https://github.com/langchain-ai/langchain/issues/4991 | 1,717,534,417 | 4,991 |
[
"langchain-ai",
"langchain"
] | ### System Info
aiohttp==3.8.4
aiosignal==1.3.1
altair==4.2.2
anyio @ file:///C:/ci_311/anyio_1676425491996/work/dist
argilla==1.6.0
argon2-cffi @ file:///opt/conda/conda-bld/argon2-cffi_1645000214183/work
argon2-cffi-bindings @ file:///C:/ci_311/argon2-cffi-bindings_1676424443321/work
asttokens @ file:///opt/conda/conda-bld/asttokens_1646925590279/work
async-timeout==4.0.2
attrs @ file:///C:/ci_311/attrs_1676422272484/work
Babel @ file:///C:/ci_311/babel_1676427169844/work
backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work
backoff==2.2.1
beautifulsoup4 @ file:///C:/b/abs_0agyz1wsr4/croot/beautifulsoup4-split_1681493048687/work
bleach @ file:///opt/conda/conda-bld/bleach_1641577558959/work
blinker==1.6.2
brotlipy==0.7.0
cachetools==5.3.0
certifi @ file:///C:/b/abs_4a0polqwty/croot/certifi_1683875377622/work/certifi
cffi @ file:///C:/ci_311/cffi_1676423759166/work
chardet==5.1.0
charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work
chromadb==0.3.21
click==8.1.3
clickhouse-connect==0.5.22
colorama @ file:///C:/ci_311/colorama_1676422310965/work
comm @ file:///C:/ci_311/comm_1678376562840/work
commonmark==0.9.1
cryptography @ file:///C:/ci_311/cryptography_1679419210767/work
Cython==0.29.34
dataclasses-json==0.5.7
debugpy @ file:///C:/ci_311/debugpy_1676426137692/work
decorator @ file:///opt/conda/conda-bld/decorator_1643638310831/work
defusedxml @ file:///tmp/build/80754af9/defusedxml_1615228127516/work
Deprecated==1.2.13
duckdb==0.7.1
entrypoints @ file:///C:/ci_311/entrypoints_1676423328987/work
et-xmlfile==1.1.0
executing @ file:///opt/conda/conda-bld/executing_1646925071911/work
fastapi==0.95.1
fastjsonschema @ file:///C:/ci_311/python-fastjsonschema_1679500568724/work
filelock==3.12.0
frozenlist==1.3.3
fsspec==2023.4.0
ftfy==6.1.1
gitdb==4.0.10
GitPython==3.1.31
google-api-core==2.11.0
google-api-python-client==2.86.0
google-auth==2.18.0
google-auth-httplib2==0.1.0
googleapis-common-protos==1.59.0
greenlet==2.0.2
h11==0.14.0
hnswlib @ file:///D:/bld/hnswlib_1675802891722/work
httpcore==0.16.3
httplib2==0.22.0
httptools==0.5.0
httpx==0.23.3
huggingface-hub==0.14.1
idna @ file:///C:/ci_311/idna_1676424932545/work
importlib-metadata==6.6.0
ipykernel @ file:///C:/ci_311/ipykernel_1678734799670/work
ipython @ file:///C:/b/abs_d1yx5tjhli/croot/ipython_1680701887259/work
ipython-genutils @ file:///tmp/build/80754af9/ipython_genutils_1606773439826/work
ipywidgets @ file:///C:/b/abs_5awapknmz_/croot/ipywidgets_1679394824767/work
jedi @ file:///C:/ci_311/jedi_1679427407646/work
Jinja2 @ file:///C:/ci_311/jinja2_1676424968965/work
joblib==1.2.0
json5 @ file:///tmp/build/80754af9/json5_1624432770122/work
jsonschema @ file:///C:/b/abs_d40z05b6r1/croot/jsonschema_1678983446576/work
jupyter @ file:///C:/ci_311/jupyter_1678249952587/work
jupyter-console @ file:///C:/b/abs_82xaa6i2y4/croot/jupyter_console_1680000189372/work
jupyter-server @ file:///C:/ci_311/jupyter_server_1678228762759/work
jupyter_client @ file:///C:/b/abs_059idvdagk/croot/jupyter_client_1680171872444/work
jupyter_core @ file:///C:/b/abs_9d0ttho3bs/croot/jupyter_core_1679906581955/work
jupyterlab @ file:///C:/ci_311/jupyterlab_1677719392688/work
jupyterlab-pygments @ file:///tmp/build/80754af9/jupyterlab_pygments_1601490720602/work
jupyterlab-widgets @ file:///C:/b/abs_38ad427jkz/croot/jupyterlab_widgets_1679055289211/work
jupyterlab_server @ file:///C:/b/abs_e0qqsihjvl/croot/jupyterlab_server_1680792526136/work
langchain==0.0.157
lxml @ file:///C:/b/abs_c2bg6ck92l/croot/lxml_1679646459966/work
lz4==4.3.2
Markdown==3.4.3
MarkupSafe @ file:///C:/ci_311/markupsafe_1676424152318/work
marshmallow==3.19.0
marshmallow-enum==1.5.1
matplotlib-inline @ file:///C:/ci_311/matplotlib-inline_1676425798036/work
mistune @ file:///C:/ci_311/mistune_1676425149302/work
monotonic==1.6
mpmath==1.3.0
msg-parser==1.2.0
multidict==6.0.4
mypy-extensions==1.0.0
nbclassic @ file:///C:/b/abs_c8_rs7b3zw/croot/nbclassic_1681756186106/work
nbclient @ file:///C:/ci_311/nbclient_1676425195918/work
nbconvert @ file:///C:/ci_311/nbconvert_1676425836196/work
nbformat @ file:///C:/ci_311/nbformat_1676424215945/work
nest-asyncio @ file:///C:/ci_311/nest-asyncio_1676423519896/work
networkx==3.1
nltk==3.8.1
notebook @ file:///C:/b/abs_49d8mc_lpe/croot/notebook_1681756182078/work
notebook_shim @ file:///C:/ci_311/notebook-shim_1678144850856/work
numexpr==2.8.4
numpy==1.23.5
olefile==0.46
openai==0.27.6
openapi-schema-pydantic==1.2.4
openpyxl==3.1.2
packaging @ file:///C:/b/abs_ed_kb9w6g4/croot/packaging_1678965418855/work
pandas==1.5.3
pandocfilters @ file:///opt/conda/conda-bld/pandocfilters_1643405455980/work
parso @ file:///opt/conda/conda-bld/parso_1641458642106/work
pdfminer.six==20221105
pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work
Pillow==9.5.0
platformdirs @ file:///C:/ci_311/platformdirs_1676422658103/work
ply==3.11
posthog==3.0.1
prometheus-client @ file:///C:/ci_311/prometheus_client_1679591942558/work
prompt-toolkit @ file:///C:/ci_311/prompt-toolkit_1676425940920/work
protobuf==3.20.3
psutil @ file:///C:/ci_311_rebuilds/psutil_1679005906571/work
pure-eval @ file:///opt/conda/conda-bld/pure_eval_1646925070566/work
pyarrow==11.0.0
pyasn1==0.5.0
pyasn1-modules==0.3.0
pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work
pydantic==1.10.7
pydeck==0.8.1b0
Pygments @ file:///opt/conda/conda-bld/pygments_1644249106324/work
Pympler==1.0.1
pyOpenSSL @ file:///C:/b/abs_de215ipd18/croot/pyopenssl_1678965319166/work
pypandoc==1.11
pyparsing==3.0.9
PyQt5==5.15.7
PyQt5-sip @ file:///C:/ci_311/pyqt-split_1676428895938/work/pyqt_sip
pyrsistent @ file:///C:/ci_311/pyrsistent_1676422695500/work
PySocks @ file:///C:/ci_311/pysocks_1676425991111/work
python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
python-docx==0.8.11
python-dotenv==1.0.0
python-magic==0.4.27
python-pptx==0.6.21
pytz @ file:///C:/ci_311/pytz_1676427070848/work
pytz-deprecation-shim==0.1.0.post0
pywin32==305.1
pywinpty @ file:///C:/ci_311/pywinpty_1677707791185/work/target/wheels/pywinpty-2.0.10-cp311-none-win_amd64.whl
PyYAML==6.0
pyzmq @ file:///C:/b/abs_8b16zbmf46/croot/pyzmq_1682697651374/work
qtconsole @ file:///C:/b/abs_eb4u9jg07y/croot/qtconsole_1681402843494/work
QtPy @ file:///C:/ci_311/qtpy_1676432558504/work
regex==2023.3.23
requests @ file:///C:/b/abs_41owkd5ymz/croot/requests_1682607524657/work
rfc3986==1.5.0
rich==13.0.1
rsa==4.9
scikit-learn==1.2.2
scipy==1.10.1
Send2Trash @ file:///tmp/build/80754af9/send2trash_1632406701022/work
sentence-transformers==2.2.2
sentencepiece==0.1.99
sip @ file:///C:/ci_311/sip_1676427825172/work
six @ file:///tmp/build/80754af9/six_1644875935023/work
smmap==5.0.0
sniffio @ file:///C:/ci_311/sniffio_1676425339093/work
soupsieve @ file:///C:/b/abs_a989exj3q6/croot/soupsieve_1680518492466/work
SQLAlchemy==2.0.12
stack-data @ file:///opt/conda/conda-bld/stack_data_1646927590127/work
starlette==0.26.1
streamlit==1.22.0
sympy==1.11.1
tenacity==8.2.2
terminado @ file:///C:/ci_311/terminado_1678228513830/work
threadpoolctl==3.1.0
tiktoken==0.3.3
tinycss2 @ file:///C:/ci_311/tinycss2_1676425376744/work
tokenizers==0.13.3
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
tomli @ file:///C:/ci_311/tomli_1676422027483/work
toolz==0.12.0
torch==2.0.0
torchvision==0.15.1
tornado @ file:///C:/ci_311/tornado_1676423689414/work
tqdm==4.65.0
traitlets @ file:///C:/ci_311/traitlets_1676423290727/work
transformers==4.28.1
typing-inspect==0.8.0
typing_extensions @ file:///C:/b/abs_a1bb332wcs/croot/typing_extensions_1681939523095/work
tzdata==2023.3
tzlocal==4.3
unstructured==0.6.2
uritemplate==4.1.1
urllib3 @ file:///C:/b/abs_3ce53vrdcr/croot/urllib3_1680254693505/work
uvicorn==0.22.0
validators==0.20.0
watchdog==3.0.0
watchfiles==0.19.0
wcwidth @ file:///Users/ktietz/demo/mc3/conda-bld/wcwidth_1629357192024/work
webencodings==0.5.1
websocket-client @ file:///C:/ci_311/websocket-client_1676426063281/work
websockets==11.0.2
widgetsnbextension @ file:///C:/b/abs_882k4_4kdf/croot/widgetsnbextension_1679313880295/work
wikipedia==1.4.0
win-inet-pton @ file:///C:/ci_311/win_inet_pton_1676425458225/work
wrapt==1.14.1
XlsxWriter==3.1.0
yarl==1.9.2
zipp==3.15.0
zstandard==0.21.0
### Who can help?
@vowelparrot
I have created a toolkit from chroma documents. I have also created a custom tool.
I want to create a vectorstoreinfo from my custom tool so I can use this tool in vectorstoreagent.
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.agents.agent_toolkits import (
create_vectorstore_agent,
VectorStoreToolkit,
VectorStoreInfo,
)
vectorstore_info = VectorStoreInfo(
name=" tax queries",
description="tax related documents",
vectorstore=db_store
)
toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)
agent_executor = create_vectorstore_agent(
llm=llm,
toolkit=toolkit,
verbose=True
)
```
### Expected behavior
I have created a custom tool
```
def evaluate expression:
ans = "expression"
return ans
tool = Tool(
name = "Expression Evaluator",
func=evaluate_expression,
description= "useful for when you need output expression.
)
```
Please, inform How can I create a vectorstoreinfo using this tool and add in a toolkit so I can use this tool in vectorstoreagent. | How to create a vectorstoreinfo using a custom tool. | https://api.github.com/repos/langchain-ai/langchain/issues/4980/comments | 3 | 2023-05-19T12:11:25Z | 2023-09-15T16:12:26Z | https://github.com/langchain-ai/langchain/issues/4980 | 1,717,149,421 | 4,980 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain==0.0.173
### 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
- [X] 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.evaluation.qa import QAGenerateChain
from langchain.chains.loading import load_chain
example_gen_chain = QAGenerateChain.from_llm(OpenAI())
example_gen_chain.save("/Users/liang.zhang/qa_gen_chain.yaml")
loaded_chain = load_chain("/Users/liang.zhang/qa_gen_chain.yaml")
```
Error:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Input In [13], in <cell line: 2>()
1 from langchain.chains.loading import load_chain
----> 2 loaded_chain = load_chain("/Users/liang.zhang/qa_gen_chain.yaml")
File ~/miniforge3/envs/mlflow-3.8/lib/python3.8/site-packages/langchain/chains/loading.py:449, in load_chain(path, **kwargs)
447 return hub_result
448 else:
--> 449 return _load_chain_from_file(path, **kwargs)
File ~/miniforge3/envs/mlflow-3.8/lib/python3.8/site-packages/langchain/chains/loading.py:476, in _load_chain_from_file(file, **kwargs)
473 config["memory"] = kwargs.pop("memory")
475 # Load the chain from the config now.
--> 476 return load_chain_from_config(config, **kwargs)
File ~/miniforge3/envs/mlflow-3.8/lib/python3.8/site-packages/langchain/chains/loading.py:439, in load_chain_from_config(config, **kwargs)
436 raise ValueError(f"Loading {config_type} chain not supported")
438 chain_loader = type_to_loader_dict[config_type]
--> 439 return chain_loader(config, **kwargs)
File ~/miniforge3/envs/mlflow-3.8/lib/python3.8/site-packages/langchain/chains/loading.py:44, in _load_llm_chain(config, **kwargs)
42 if "prompt" in config:
43 prompt_config = config.pop("prompt")
---> 44 prompt = load_prompt_from_config(prompt_config)
45 elif "prompt_path" in config:
46 prompt = load_prompt(config.pop("prompt_path"))
File ~/miniforge3/envs/mlflow-3.8/lib/python3.8/site-packages/langchain/prompts/loading.py:30, in load_prompt_from_config(config)
27 raise ValueError(f"Loading {config_type} prompt not supported")
29 prompt_loader = type_to_loader_dict[config_type]
---> 30 return prompt_loader(config)
File ~/miniforge3/envs/mlflow-3.8/lib/python3.8/site-packages/langchain/prompts/loading.py:115, in _load_prompt(config)
113 config = _load_template("template", config)
114 config = _load_output_parser(config)
--> 115 return PromptTemplate(**config)
File ~/miniforge3/envs/mlflow-3.8/lib/python3.8/site-packages/pydantic/main.py:342, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for PromptTemplate
output_parser
Can't instantiate abstract class BaseOutputParser with abstract methods parse (type=type_error)
```
### Expected behavior
No errors should occur. | QAGenerateChain cannot be loaded | https://api.github.com/repos/langchain-ai/langchain/issues/4977/comments | 2 | 2023-05-19T11:38:43Z | 2023-05-21T22:56:09Z | https://github.com/langchain-ai/langchain/issues/4977 | 1,717,108,430 | 4,977 |
[
"langchain-ai",
"langchain"
] | ### Feature request
https://arxiv.org/pdf/2305.10601.pdf
### Motivation
Language models are increasingly being deployed for general problem-solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, there is a new framework for language model inference, “Tree of Thoughts” (ToT), which generalizes over the popular “Chain of Thought” approach to prompting language models and enables exploration over coherent units of text (“thoughts”) that serve as intermediate steps toward problem-solving. ToT allows LMs to perform deliberate decision-making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices.
### Your contribution
maybe, not sure😥 | consider introduce ToT approch in langchain🐱🐱 | https://api.github.com/repos/langchain-ai/langchain/issues/4975/comments | 23 | 2023-05-19T10:50:30Z | 2023-08-17T20:58:11Z | https://github.com/langchain-ai/langchain/issues/4975 | 1,717,046,200 | 4,975 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
Hi!
Can you change the version of pydantic in the dependencies? There is a vulnerability in this version, because it depends on pydantic. I can't install new versions of langchain because they don't pass the security check on my PC.
https://github.com/kuimono/openapi-schema-pydantic/issues/31
https://nvd.nist.gov/vuln/detail/CVE-2021-29510
### Suggestion:
Remove openapi-schema-pydantic from dependency?! | Issue: openapi-schema-pydantic vulnerability | https://api.github.com/repos/langchain-ai/langchain/issues/4974/comments | 3 | 2023-05-19T10:39:37Z | 2024-03-13T16:12:28Z | https://github.com/langchain-ai/langchain/issues/4974 | 1,717,032,053 | 4,974 |
[
"langchain-ai",
"langchain"
] | ### System Info
Langchain version: 0.0.173
Platform: Ubuntu 22.04.2 LTS
Python: 3.10.6
### 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
Firstly, you can reproduce the errors using the following jupyter notebook, assumed that you have all the dependencies setup:
https://github.com/limcheekin/langchain-playground/blob/main/supabase.ipynb
When running the following code:
```
retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1})
docs = retriever.get_relevant_documents("What is Flutter?")
```
It return the following errors, you can see the complete errors thread at cell 6 from the notebook above:
```
ValueError: Number of columns in X and Y must be the same. X has shape (1, 768) and Y has shape (20, 0).
```
### Expected behavior
It should return 1 doc which similar to the documentation at https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/supabase.html#maximal-marginal-relevance-searches | SupabaseVectorStore ValueError in .as_retriever(search_type="mmr").get_relevant_documents() | https://api.github.com/repos/langchain-ai/langchain/issues/4972/comments | 1 | 2023-05-19T08:38:01Z | 2023-05-19T09:03:00Z | https://github.com/langchain-ai/langchain/issues/4972 | 1,716,866,155 | 4,972 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
Hi guys,
New to github and my first post here.
Been testing out LLM + Langchain SQL agent and it's really cool.
However, I'm running into this issue constantly.
Say there are 3 types of continents in my SQL, the US, Asia, and Europe.
When I ask about 2 of them it works, but Asia kept giving me:
"The table does not have a column called "continent"
This is also right after it pulls the schema via: "Action: schema_sql_db".
You can clearly see the column name and it works for the other 2 but not this one.
Is there something I'm not getting?
Thanks.
### Suggestion:
_No response_ | Issue: The table does not have a column called $ when there clearly is. | https://api.github.com/repos/langchain-ai/langchain/issues/4968/comments | 1 | 2023-05-19T06:21:01Z | 2023-09-10T16:15:21Z | https://github.com/langchain-ai/langchain/issues/4968 | 1,716,692,511 | 4,968 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I'm trying to query from my knowledge base in csv by creating embeddings.
I was able to do it with OpenAI LLM model. But when I'm trying with AzureOpenAI model, I'm getting an error.
I've checked the LLM object for both OpenAI modela and AzureOpenAI model, attributes are all same, but I'm getting below error.
I'm using text-embeddings-ada-002 for creating embeddings and text-davinci-003 for LLM model.
It would be great if someone can give me leads. Thanks in advance.
Error:
Traceback (most recent call last):
File "C:\Users\akshay\Documents\GPT\azureLlmGPT.py", line 64, in <module>
print(model.run(question))
^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 213, in run
return self(args[0])[self.output_keys[0]]
^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 116, in __call__
raise e
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 113, in __call__
outputs = self._call(inputs)
^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\retrieval_qa\base.py", line 110, in _call
answer = self.combine_documents_chain.run(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 216, in run
return self(kwargs)[self.output_keys[0]]
^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 116, in __call__
raise e
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 113, in __call__
outputs = self._call(inputs)
^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\combine_documents\base.py", line 75, in _call
output, extra_return_dict = self.combine_docs(docs, **other_keys)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\combine_documents\map_reduce.py", line 139, in combine_docs
results = self.llm_chain.apply(
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\llm.py", line 118, in apply
response = self.generate(input_list)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\llm.py", line 62, in generate
return self.llm.generate_prompt(prompts, stop)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\llms\base.py", line 107, in generate_prompt
return self.generate(prompt_strings, stop=stop)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\llms\base.py", line 140, in generate
raise e
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\llms\base.py", line 137, in generate
output = self._generate(prompts, stop=stop)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\llms\openai.py", line 297, in _generate
response = completion_with_retry(self, prompt=_prompts, **params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\llms\openai.py", line 102, in completion_with_retry
return _completion_with_retry(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\tenacity\__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\tenacity\__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\tenacity\__init__.py", line 314, in iter
return fut.result()
^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py", line 401, in __get_result
raise self._exception
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\tenacity\__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\llms\openai.py", line 100, in _completion_with_retry
return llm.client.create(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\api_resources\completion.py", line 25, in create
return super().create(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\api_requestor.py", line 226, in request
resp, got_stream = self._interpret_response(result, stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\api_requestor.py", line 620, in _interpret_response
self._interpret_response_line(
File "C:\Users\akshay\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\api_requestor.py", line 683, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: Resource not found
### Suggestion:
_No response_ | Issue: RetrievalQA is not working with AzureOpenAI LLM model | https://api.github.com/repos/langchain-ai/langchain/issues/4966/comments | 2 | 2023-05-19T06:12:01Z | 2023-05-20T09:06:11Z | https://github.com/langchain-ai/langchain/issues/4966 | 1,716,680,713 | 4,966 |
[
"langchain-ai",
"langchain"
] | ### Feature request
support for kotlin language
### Motivation
1.Safety and Reliability: Kotlin emphasizes on safety and reliability, and it can help developers avoid common programming errors through type checks, null safety, exception handling, and other mechanisms. Additionally, Kotlin provides a functional programming style that reduces side effects and improves code reliability and maintainability.
2.Interoperability: Kotlin seamlessly interoperates with Java, which means that you can use existing Java libraries and frameworks in Kotlin projects, and Kotlin code can be used in Java projects. Moreover, Kotlin supports JavaScript and Native platforms, so you can use the same code across different platforms.
3.Conciseness: Kotlin has a concise and clear syntax that reduces code redundancy and complexity. For example, Kotlin uses lambda expressions and extension functions to simplify code, and provides many convenient syntax sugars such as null safety operator, range expressions, string templates, etc. These features can greatly improve development efficiency and reduce the possibility of errors.
### Your contribution
I can write any partial code related to Kotlin. | Is it possible to support the kotlin language?Like langchainkt. | https://api.github.com/repos/langchain-ai/langchain/issues/4963/comments | 7 | 2023-05-19T05:34:35Z | 2024-06-15T14:01:51Z | https://github.com/langchain-ai/langchain/issues/4963 | 1,716,644,366 | 4,963 |
[
"langchain-ai",
"langchain"
] | ### Issue with current documentation:
In the doc as mentioned below:-
https://python.langchain.com/en/latest/modules/chains/examples/llm_summarization_checker.html
Assumptions is misspelled as assumtions.
### Idea or request for content:
Fix the misspelling in the doc markdown. | DOC: Misspelling in LLMSummarizationCheckerChain documentation | https://api.github.com/repos/langchain-ai/langchain/issues/4959/comments | 1 | 2023-05-19T04:45:16Z | 2023-09-10T16:15:26Z | https://github.com/langchain-ai/langchain/issues/4959 | 1,716,598,909 | 4,959 |
[
"langchain-ai",
"langchain"
] | ### Feature request
- Neo4j
[Neo4j](https://github.com/neo4j/neo4j)
- langchain2neo4j
[langchain2neo4j](https://github.com/tomasonjo/langchain2neo4j)
- langchain2ongdb
[langchain2ongdb](https://github.com/ongdb-contrib/langchain2ongdb)
### Motivation
We are building some question answering tools based on the knowledge graph and it would be convenient to have some open libraries based on Neo4j.
### Your contribution
I'm trying to make a contribution:) | Consider adding the default Neo4jDatabaseChain tool to the Chains component | https://api.github.com/repos/langchain-ai/langchain/issues/4957/comments | 1 | 2023-05-19T03:33:24Z | 2023-06-08T00:41:58Z | https://github.com/langchain-ai/langchain/issues/4957 | 1,716,553,534 | 4,957 |
[
"langchain-ai",
"langchain"
] | I am trying on the Custom Agent with Tool Retrieval example, there are some times (not always 100%) when the Agent Executor will return the answer itself in the Action Input. The inconsistency make things even worse.
For example: I have a Small Talk Tool that will be in charge of answering casual conversation from user. I have given a profile to my agent (name: Sam). Here is one of the scenario that I got:
**Question**: Hello I am Bob, what is your name?
**Thought**: The user is initiating a small talk conversation
**Action Input**: Hi Bob, I am Sam, your personal assistant. How can I assist you today?
**Observation**: As an AI language model, I don't need any assistance, Sam. But thank you for asking! How about you? Is there anything I can help you with?
So the expected response by the agent became the action input, which in the end output another response which doesnt make sense at all. This goes on to happen to other tools as well such as querying content from Vectorstore.
### Suggestion:
Is there a possibility where we can restrict the action input to just the user question instead of allowing the agent to answer it and modify the initial context?
Be it the prompt or temperature? I'm open to any advice. Thanks!
| Issue: Agent Executor tends to answer user question directly and set it as Action Input during the iterative thought process. | https://api.github.com/repos/langchain-ai/langchain/issues/4955/comments | 4 | 2023-05-19T02:34:55Z | 2023-09-27T16:07:00Z | https://github.com/langchain-ai/langchain/issues/4955 | 1,716,519,016 | 4,955 |
[
"langchain-ai",
"langchain"
] | ### System Info
Langchain version: 0.0.173
Redis version: 4.5.5
Python version: 3.11
OS: Windows 10
### Who can help?
@hwchase17 @agola11
### Information
- [X] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
using latest langchain with redis vectorstore return error TypeError
code here:
```
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1)
rds = Redis.from_texts(texts, embeddings, redis_url="redis://localhost:6379", index_name=uid)
```
error stack:
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
File D:\Projects\openai\venv\Lib\site-packages\redis\connection.py:1450, in ConnectionPool.get_connection(self, command_name, *keys, **options)
1449 try:
-> 1450 connection = self._available_connections.pop()
1451 except IndexError:
IndexError: pop from empty list
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
Cell In[5], line 11
8 print("create vector store with id: {}".format(uid))
10 embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1)
---> 11 rds = Redis.from_texts(texts, embeddings, redis_url = "redis://localhost:6379",index_name=uid)
File D:\Projects\openai\venv\Lib\site-packages\langchain\vectorstores\redis.py:448, in Redis.from_texts(cls, texts, embedding, metadatas, index_name, content_key, metadata_key, vector_key, **kwargs)
419 @classmethod
420 def from_texts(
421 cls: Type[Redis],
(...)
429 **kwargs: Any,
430 ) -> Redis:
431 """Create a Redis vectorstore from raw documents.
432 This is a user-friendly interface that:
433 1. Embeds documents.
(...)
446 )
447 """
--> 448 instance, _ = cls.from_texts_return_keys(
449 texts=texts,
450 embedding=embedding,
451 metadatas=metadatas,
452 index_name=index_name,
453 content_key=content_key,
454 metadata_key=metadata_key,
455 vector_key=vector_key,
456 kwargs=kwargs,
457 )
458 return instance
File D:\Projects\openai\venv\Lib\site-packages\langchain\vectorstores\redis.py:399, in Redis.from_texts_return_keys(cls, texts, embedding, metadatas, index_name, content_key, metadata_key, vector_key, distance_metric, **kwargs)
396 index_name = uuid.uuid4().hex
398 # Create instance
--> 399 instance = cls(
400 redis_url=redis_url,
401 index_name=index_name,
402 embedding_function=embedding.embed_query,
403 content_key=content_key,
404 metadata_key=metadata_key,
405 vector_key=vector_key,
406 **kwargs,
407 )
409 # Create embeddings over documents
410 embeddings = embedding.embed_documents(texts)
File D:\Projects\openai\venv\Lib\site-packages\langchain\vectorstores\redis.py:138, in Redis.__init__(self, redis_url, index_name, embedding_function, content_key, metadata_key, vector_key, relevance_score_fn, **kwargs)
136 redis_client = redis.from_url(redis_url, **kwargs)
137 # check if redis has redisearch module installed
--> 138 _check_redis_module_exist(redis_client, REDIS_REQUIRED_MODULES)
139 except ValueError as e:
140 raise ValueError(f"Redis failed to connect: {e}")
File D:\Projects\openai\venv\Lib\site-packages\langchain\vectorstores\redis.py:48, in _check_redis_module_exist(client, required_modules)
46 def _check_redis_module_exist(client: RedisType, required_modules: List[dict]) -> None:
47 """Check if the correct Redis modules are installed."""
---> 48 installed_modules = client.module_list()
49 installed_modules = {
50 module[b"name"].decode("utf-8"): module for module in installed_modules
51 }
52 for module in required_modules:
File D:\Projects\openai\venv\Lib\site-packages\redis\commands\core.py:5781, in ModuleCommands.module_list(self)
5774 def module_list(self) -> ResponseT:
5775 """
5776 Returns a list of dictionaries containing the name and version of
5777 all loaded modules.
5778
5779 For more information see https://redis.io/commands/module-list
5780 """
-> 5781 return self.execute_command("MODULE LIST")
File D:\Projects\openai\venv\Lib\site-packages\redis\client.py:1266, in Redis.execute_command(self, *args, **options)
1264 pool = self.connection_pool
1265 command_name = args[0]
-> 1266 conn = self.connection or pool.get_connection(command_name, **options)
1268 try:
1269 return conn.retry.call_with_retry(
1270 lambda: self._send_command_parse_response(
1271 conn, command_name, *args, **options
1272 ),
1273 lambda error: self._disconnect_raise(conn, error),
1274 )
File D:\Projects\openai\venv\Lib\site-packages\redis\connection.py:1452, in ConnectionPool.get_connection(self, command_name, *keys, **options)
1450 connection = self._available_connections.pop()
1451 except IndexError:
-> 1452 connection = self.make_connection()
1453 self._in_use_connections.add(connection)
1455 try:
1456 # ensure this connection is connected to Redis
File D:\Projects\openai\venv\Lib\site-packages\redis\connection.py:1492, in ConnectionPool.make_connection(self)
1490 raise ConnectionError("Too many connections")
1491 self._created_connections += 1
-> 1492 return self.connection_class(**self.connection_kwargs)
File D:\Projects\openai\venv\Lib\site-packages\redis\connection.py:956, in Connection.__init__(self, host, port, socket_timeout, socket_connect_timeout, socket_keepalive, socket_keepalive_options, socket_type, **kwargs)
954 self.socket_keepalive_options = socket_keepalive_options or {}
955 self.socket_type = socket_type
--> 956 super().__init__(**kwargs)
TypeError: AbstractConnection.__init__() got an unexpected keyword argument 'kwargs'
```
### Expected behavior
I followed the instructions in the documentation and it ran fine before, but when I upgraded the langchain version to the latest version, it failed.
I believe this is related to https://github.com/hwchase17/langchain/pull/4857, am I using this method incorrectly now? I'm new to langchain. | vectorstore redis return TypeError | https://api.github.com/repos/langchain-ai/langchain/issues/4952/comments | 16 | 2023-05-19T00:53:22Z | 2023-12-12T16:31:54Z | https://github.com/langchain-ai/langchain/issues/4952 | 1,716,453,446 | 4,952 |
[
"langchain-ai",
"langchain"
] | ## Question
I'm interested in creating a conversational app using RetrievalQA that can also answer using external knowledge. However, I'm curious whether RetrievalQA supports replying in a streaming manner. I couldn't find any related articles, so I would like to ask everyone here. | Issue: Does RetrievalQA Support Streaming Replies? | https://api.github.com/repos/langchain-ai/langchain/issues/4950/comments | 30 | 2023-05-19T00:13:39Z | 2024-08-05T11:55:14Z | https://github.com/langchain-ai/langchain/issues/4950 | 1,716,429,367 | 4,950 |
[
"langchain-ai",
"langchain"
] | ### System Info
Colab standard Python 3 backend
### Who can help?
@O-Roma
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Open GraphQL example notebook in colab
2. Add langchain and openai to the pip install statement
3. add environment variable for OPENAI_API_KEY
4. Run the notebook
Got the following error
```
ValidationError: 3 validation errors for GraphQLAPIWrapper
gql_client
field required (type=value_error.missing)
gql_function
field required (type=value_error.missing)
__root__
Could not import gql python package. Please install it with `pip install gql`. (type=value_error)
```
### Expected behavior
The example should probably work. | GraphQL example notebook not working | https://api.github.com/repos/langchain-ai/langchain/issues/4946/comments | 4 | 2023-05-18T21:46:51Z | 2023-09-12T17:06:03Z | https://github.com/langchain-ai/langchain/issues/4946 | 1,716,309,110 | 4,946 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I am using Python Flask app for chat over data. So in the console I am getting streamable response directly from the OpenAI since I can enable streming with a flag streaming=True.
The problem is, that I can't “forward” the stream or “show” the strem than in my API call.
Code for the processing OpenAI and chain is:
```
def askQuestion(self, collection_id, question):
collection_name = "collection-" + str(collection_id)
self.llm = ChatOpenAI(model_name=self.model_name, temperature=self.temperature, openai_api_key=os.environ.get('OPENAI_API_KEY'), streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key='answer')
chroma_Vectorstore = Chroma(collection_name=collection_name, embedding_function=self.embeddingsOpenAi, client=self.chroma_client)
self.chain = ConversationalRetrievalChain.from_llm(self.llm, chroma_Vectorstore.as_retriever(similarity_search_with_score=True),
return_source_documents=True,verbose=VERBOSE,
memory=self.memory)
result = self.chain({"question": question})
res_dict = {
"answer": result["answer"],
}
res_dict["source_documents"] = []
for source in result["source_documents"]:
res_dict["source_documents"].append({
"page_content": source.page_content,
"metadata": source.metadata
})
return res_dict`
```
and the API route code:
```
def stream(collection_id, question):
completion = document_thread.askQuestion(collection_id, question)
for line in completion:
yield 'data: %s\n\n' % line
@app.route("/collection/<int:collection_id>/ask_question", methods=["POST"])
@stream_with_context
def ask_question(collection_id):
question = request.form["question"]
# response_generator = document_thread.askQuestion(collection_id, question)
# return jsonify(response_generator)
def stream(question):
completion = document_thread.askQuestion(collection_id, question)
for line in completion['answer']:
yield line
return Response(stream(question), mimetype='text/event-stream')
```
I am testing my endpoint with curl and I am passing flag -N to the curl, so I should get the streamable response, if it is possible.
When I make API call first the endpoint is waiting to process the data (I can see in my terminal in VS code the streamable answer) and when finished, I get everything displayed in one go.
Thanks
### Suggestion:
_No response_ | Issue: Stream a response from LangChain's OpenAI with Pyton Flask API | https://api.github.com/repos/langchain-ai/langchain/issues/4945/comments | 30 | 2023-05-18T21:41:40Z | 2024-03-07T21:26:59Z | https://github.com/langchain-ai/langchain/issues/4945 | 1,716,304,777 | 4,945 |
[
"langchain-ai",
"langchain"
] | ### System Info
0.1173
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1: Create Chroma vectorstore
2: Persist vectorstore
3: Use vectorstore once
4: Vectorstore no longer works, says "Index not found"
### Expected behavior
It works. | False "Index not found" messages | https://api.github.com/repos/langchain-ai/langchain/issues/4944/comments | 8 | 2023-05-18T21:10:59Z | 2023-09-19T16:10:07Z | https://github.com/langchain-ai/langchain/issues/4944 | 1,716,269,896 | 4,944 |
[
"langchain-ai",
"langchain"
] | ### Feature request
FLARE is providing some more accurate output when it works. I would love to switch to it. However, I do find that it often breaches the 4k context length.
I still don't have GPT4 access because I am a pleb (approve me plz OpenAI).
The stuff / map-reduce / refine chains used in the retrieval QandA would be great here. The FLARE chain looks like it is doing something akin to the "stuff" chain by just concatenating all the documents it found.
It would be nice if the LLMCombine chain was optional and defaulted to "stuff" like the retrieval chains do. This could be an option to handle context window issues when using FLARE. Ideally the same API followed, were users just pass the "chain_type" kwarg.
The 4k context window is unfortunately making FLARE unworkable.
P.S. also interested in seeing if sources could be added to FLARE.
### Motivation
I want to switch my retreival chain to be FLARE based (ideally with sources in the future). However, the context window limitations are making it unworkable at the moment. I am not even using chat_history.
### Your contribution
Happy to work with someone on this to raise a PR. | FLARE | Add CombineChain to handle long context | https://api.github.com/repos/langchain-ai/langchain/issues/4943/comments | 3 | 2023-05-18T20:04:50Z | 2023-09-15T16:12:36Z | https://github.com/langchain-ai/langchain/issues/4943 | 1,716,194,737 | 4,943 |
[
"langchain-ai",
"langchain"
] | ### System Info
Langchain version 0.0.162
python: 3.10
My all .eml email files are of 4.99 GB in total. Number of Documents generated are 5900000 .. and there are around 7 million tokens generated .
I have 2 questions:
1) since the documents contain simple plain text, why are there so many tokens generated? I mean 7 million tokens :/
2) What can i do to reduce these number of tokens will data sampling help , should i index in batches of 1000?
code:
```
print('loading docs from directory ...')
loader = DirectoryLoader('./test2',loader_cls=UnstructuredEmailLoader)
raw_documents = loader.load()
print('loaded docs with length')
#Splitting documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(raw_documents)
# Changing source to point to the original document
for x in documents:
x.page_content = x.page_content.replace("\n\n", " ")
x.page_content = x.page_content.replace("\n", " ")
x.metadata["source"] = x.metadata["source"].replace("test\\", "")
x.metadata["source"] = x.metadata["source"].replace("semails\\", "")
x.metadata["source"] = x.metadata["source"].replace("test2\\", "")
print(x)
# Creating index and saving it to disk
print("Creating index")
print("length of documents: "+str(len(documents)))
db = FAISS.from_documents(documents, embeddings )
print("Index created, saving to disk")
db.save_local("./index")
```
### Who can help?
@hwchase17
@vowelparrot
### 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
-
### Expected behavior
Token should not be more than a million | loading .eml Email files of 5 GB as documents generating large number of tokens (7 million) on index creation | https://api.github.com/repos/langchain-ai/langchain/issues/4942/comments | 1 | 2023-05-18T18:54:10Z | 2023-09-10T16:15:38Z | https://github.com/langchain-ai/langchain/issues/4942 | 1,716,110,918 | 4,942 |
[
"langchain-ai",
"langchain"
] | ### System Info
While loading an already existing index with existing openAI embeddings (data indexed using haystack framework)
```python
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url=es_url,
index_name=index,
embedding=embeddings
)
```
Running below command
```python
elastic_vector_search.similarity_search(query)
```
gives bellow error
```python
---------------------------------------------------------------------------
BadRequestError Traceback (most recent call last)
Cell In[88], line 2
1 query = "adnoc distribution"
----> 2 elastic_vector_search.similarity_search(query)
File /opt/homebrew/Caskroom/miniforge/base/envs/langchain/lib/python3.11/site-packages/langchain/vectorstores/elastic_vector_search.py:206, in ElasticVectorSearch.similarity_search(self, query, k, filter, **kwargs)
194 def similarity_search(
195 self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
196 ) -> List[Document]:
197 """Return docs most similar to query.
198
199 Args:
(...)
204 List of Documents most similar to the query.
205 """
--> 206 docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
207 documents = [d[0] for d in docs_and_scores]
208 return documents
File /opt/homebrew/Caskroom/miniforge/base/envs/langchain/lib/python3.11/site-packages/langchain/vectorstores/elastic_vector_search.py:222, in ElasticVectorSearch.similarity_search_with_score(self, query, k, filter, **kwargs)
220 embedding = self.embedding.embed_query(query)
221 script_query = _default_script_query(embedding, filter)
--> 222 response = self.client.search(index=self.index_name, query=script_query, size=k)
223 hits = [hit for hit in response["hits"]["hits"]]
224 docs_and_scores = [
225 (
226 Document(
(...)
232 for hit in hits
233 ]
File /opt/homebrew/Caskroom/miniforge/base/envs/langchain/lib/python3.11/site-packages/elasticsearch/_sync/client/utils.py:414, in _rewrite_parameters.<locals>.wrapper.<locals>.wrapped(*args, **kwargs)
411 except KeyError:
412 pass
--> 414 return api(*args, **kwargs)
File /opt/homebrew/Caskroom/miniforge/base/envs/langchain/lib/python3.11/site-packages/elasticsearch/_sync/client/__init__.py:3857, in Elasticsearch.search(self, index, aggregations, aggs, allow_no_indices, allow_partial_search_results, analyze_wildcard, analyzer, batched_reduce_size, ccs_minimize_roundtrips, collapse, default_operator, df, docvalue_fields, error_trace, expand_wildcards, explain, ext, fields, filter_path, from_, highlight, human, ignore_throttled, ignore_unavailable, indices_boost, knn, lenient, max_concurrent_shard_requests, min_compatible_shard_node, min_score, pit, post_filter, pre_filter_shard_size, preference, pretty, profile, q, query, request_cache, rescore, rest_total_hits_as_int, routing, runtime_mappings, script_fields, scroll, search_after, search_type, seq_no_primary_term, size, slice, sort, source, source_excludes, source_includes, stats, stored_fields, suggest, suggest_field, suggest_mode, suggest_size, suggest_text, terminate_after, timeout, track_scores, track_total_hits, typed_keys, version)
3855 if __body is not None:
3856 __headers["content-type"] = "application/json"
-> 3857 return self.perform_request( # type: ignore[return-value]
3858 "POST", __path, params=__query, headers=__headers, body=__body
3859 )
File /opt/homebrew/Caskroom/miniforge/base/envs/langchain/lib/python3.11/site-packages/elasticsearch/_sync/client/_base.py:320, in BaseClient.perform_request(self, method, path, params, headers, body)
317 except (ValueError, KeyError, TypeError):
318 pass
--> 320 raise HTTP_EXCEPTIONS.get(meta.status, ApiError)(
321 message=message, meta=meta, body=resp_body
322 )
324 # 'X-Elastic-Product: Elasticsearch' should be on every 2XX response.
325 if not self._verified_elasticsearch:
326 # If the header is set we mark the server as verified.
BadRequestError: BadRequestError(400, 'search_phase_execution_exception', 'runtime error')
```
If an index with embeddings(in this case openAI) is not created using langchain , will it not be loaded as a vector_store ?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the steps shown earlier
### Expected behavior
Eventhough we do not create an elasticsearch index using langchain, an existing index with vector embeddings, should be able to load. | Error while loading saved index in Elasticsearch | https://api.github.com/repos/langchain-ai/langchain/issues/4940/comments | 1 | 2023-05-18T17:34:22Z | 2023-09-15T22:12:58Z | https://github.com/langchain-ai/langchain/issues/4940 | 1,716,005,900 | 4,940 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
The author of the issue/PR cannot add a label to it.
Of course, some labels should be accessible only with high privilege (like `bug`(?), `close`) but some should be accessible for everybody. Like `agent`, `chains`, and `documentation`.
The label is a powerful tool to filter out the issues/PR but is not accessible.
### Suggestion:
Allow the use of most of the `labels` to everybody in `Issues`/`pull requests`. | Issue: Filling an issue or PR with `label(s)` | https://api.github.com/repos/langchain-ai/langchain/issues/4934/comments | 3 | 2023-05-18T16:09:57Z | 2023-08-31T17:08:05Z | https://github.com/langchain-ai/langchain/issues/4934 | 1,715,894,918 | 4,934 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain-0.0.173
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am using the same code provided here. [](https://python.langchain.com/en/latest/modules/models/text_embedding/examples/azureopenai.html)
My code is like:
```
from langchain.embeddings.openai import OpenAIEmbeddings
# open ai key
openai.api_type = "azure"
openai.api_version = "2023-03-15-preview"
openai.api_base = 'https://xxxxxopenai.openai.azure.com/'
openai.api_key = "xxxxxxxxxxxxxxxxxxxxxxxx"
embeddings = OpenAIEmbeddings(deployment='Embedding_Generation' )
print(embeddings.embed_query("Hi How are You"))
```
I also tried other options but getting the same error on print statement.
AuthenticationError: Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.
### Expected behavior
I want to generate the embneddings. | langchain AzureOpenAI Embeddings | https://api.github.com/repos/langchain-ai/langchain/issues/4925/comments | 3 | 2023-05-18T12:56:40Z | 2023-05-19T20:11:46Z | https://github.com/langchain-ai/langchain/issues/4925 | 1,715,594,468 | 4,925 |
[
"langchain-ai",
"langchain"
] | ### System Info
The llm model using default davinci example provided in https://python.langchain.com/en/latest/ecosystem/wandb_tracking.html is OK. But seems not able to handle ChatOpenAI object. How to solve this?
The code is as below:
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code:
```python
from langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandler
wandb_callback = WandbCallbackHandler(
job_type="inference",
project="langchain_callback_demo",
group="test_group",
name="llm",
tags=["test"],
)
callbacks = [StdOutCallbackHandler(), wandb_callback]
chat = ChatOpenAI(model_name='gpt-3.5-turbo', callbacks=callbacks, temperature=0, request_timeout=20)
resp = chat([HumanMessage(content="Write me 4 greeting sentences.")])
wandb_callback.flush_tracker(chat, name="simple")
```
### Expected behavior
Error:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[3], line 4
2 chat = ChatOpenAI(model_name='gpt-3.5-turbo', callbacks=callbacks, temperature=0, request_timeout=20)
3 resp = chat([HumanMessage(content="Write me 4 greeting sentences.")])
----> 4 wandb_callback.flush_tracker(chat, name="simple")
File [~/miniconda3/envs/lang/lib/python3.11/site-packages/langchain/callbacks/wandb_callback.py:560](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/ocean/projects/behavior/~/miniconda3/envs/lang/lib/python3.11/site-packages/langchain/callbacks/wandb_callback.py:560), in WandbCallbackHandler.flush_tracker(self, langchain_asset, reset, finish, job_type, project, entity, tags, group, name, notes, visualize, complexity_metrics)
558 model_artifact.add(session_analysis_table, name="session_analysis")
559 try:
--> 560 langchain_asset.save(langchain_asset_path)
561 model_artifact.add_file(str(langchain_asset_path))
562 model_artifact.metadata = load_json_to_dict(langchain_asset_path)
AttributeError: 'ChatOpenAI' object has no attribute 'save'
``` | Seems Langchain Wandb can not handle the ChatOpenAI object? | https://api.github.com/repos/langchain-ai/langchain/issues/4922/comments | 6 | 2023-05-18T11:51:57Z | 2023-09-14T03:11:01Z | https://github.com/langchain-ai/langchain/issues/4922 | 1,715,504,366 | 4,922 |
[
"langchain-ai",
"langchain"
] | ### System Info
windows 11, anaconda, langchain .__version__: '0.0.171' python 3.9.16
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hi, I am trying to build a custom tool that will take some text as input prompt it with some specific prompt and output the prompted text. When doing so the output text is correct do I get an error in the end here is the code:
```python
@tool("optimistic_string")
def optimistic_string(input_string: str) -> str:
"""use when unsure about other options"""
# Add your logic to process the input_string and generate the output_string
prompt = "Rewrite the following sentence with a more optimistic tone: {{input_string}}"
output_string = llm.generate(prompt) # Replace this with the actual call to the language model
return "output_string"
tools = [optimistic_string]
# Initialize the agent with the custom tool
llm = ChatOpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Run the agent with an example input
result = agent.run( "I'm not sure about the future.")
print(result)
```
Here is the error:
```
> Entering new AgentExecutor chain...
I should try to be optimistic and see the positive possibilities.
Action: optimistic_string
Action Input: "I'm excited to see what the future holds!"Traceback (most recent call last):
…
File ~\anaconda3\envs\aagi\lib\site-packages\langchain\chat_models\openai.py:307 in <listcomp>
message_dicts = [_convert_message_to_dict(m) for m in messages]
File ~\anaconda3\envs\aagi\lib\site-packages\langchain\chat_models\openai.py:92 in _convert_message_to_dict
raise ValueError(f"Got unknown type {message}")
ValueError: Got unknown type R
```
### Expected behavior
Just output the text | ValueError: Got unknown type R | https://api.github.com/repos/langchain-ai/langchain/issues/4921/comments | 1 | 2023-05-18T11:32:44Z | 2023-05-18T14:51:39Z | https://github.com/langchain-ai/langchain/issues/4921 | 1,715,480,235 | 4,921 |
[
"langchain-ai",
"langchain"
] | ### System Info
Below is the code which I have written
```
from langchain.document_loaders import ImageCaptionLoader
import transformers
import pytessearct
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
loader = ImageCaptionLoader(r"C:\Users\q,k and v in self-attention.PNG")
list_docs = loader.load()
list_docs
```
The above code is returning the below output, it is saying transformers package not found, but it is already installed and import is running.
```
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
File ~\anaconda3\lib\site-packages\langchain\document_loaders\image_captions.py:40, in ImageCaptionLoader.load(self)
39 try:
---> 40 from transformers import BlipForConditionalGeneration, BlipProcessor
41 except ImportError:
ImportError: cannot import name 'BlipForConditionalGeneration' from 'transformers' (C:\Users\nithi\anaconda3\lib\site-packages\transformers\__init__.py)
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
Cell In[4], line 2
1 loader = ImageCaptionLoader(r"C:\Users\q,k and v in self-attention.PNG")
----> 2 list_docs = loader.load()
3 list_docs
File ~\anaconda3\lib\site-packages\langchain\document_loaders\image_captions.py:42, in ImageCaptionLoader.load(self)
40 from transformers import BlipForConditionalGeneration, BlipProcessor
41 except ImportError:
---> 42 raise ValueError(
43 "transformers package not found, please install with"
44 "`pip install transformers`"
45 )
47 processor = BlipProcessor.from_pretrained(self.blip_processor)
48 model = BlipForConditionalGeneration.from_pretrained(self.blip_model)
ValueError: transformers package not found, please install with`pip install transformers`
```
Can anyone help me with this?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
-
### Expected behavior
I'm unable to load the image using ImageCaptionLoader. | Unable to use ImageCaptionLoader function | https://api.github.com/repos/langchain-ai/langchain/issues/4919/comments | 4 | 2023-05-18T11:24:00Z | 2023-11-01T16:07:25Z | https://github.com/langchain-ai/langchain/issues/4919 | 1,715,469,726 | 4,919 |
[
"langchain-ai",
"langchain"
] | ### System Info
How to enable precise formatting for the input of a tools utility class, such as accepting data of type list[str]? Is this achievable?
```python
class QueryTokenAssetsInput(BaseModel):
addresses: str = Field(description="like 'address1,address2,address3',should be a list of addresses")
@tool("query_token_assets", return_direct=True, args_schema=QueryTokenAssetsInput)
def query_prices(addresses: str):
"""query the prices of tokens"""
addresses = re.findall(r'"([^"]*)', addresses)[0].split(',')
addresses = [address.replace(" ",'') for address in addresses]
data = fetcher.query_tokens_price(addresses=addresses)
return data
```
Just like the code snippet provided above, when the input "addresses" is a string and the question posed to the large model is about `the price of "0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599, 0xae7ab96520DE3A18E5e111B5EaAb095312D7fE84, 0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48"`, it interprets "addresses" as `'addresses = "0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599,0xae7ab96520DE3A18E5e111B5EaAb095312D7fE84'"`. This requires me to use regular expressions to parse the string and convert it into `['0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599', '0xae7ab96520DE3A18E5e111B5EaAb095312D7fE84']`, but sometimes "addresses" is not very consistent, leading to bugs in my regular expression parsing. How can I make the input for "addresses" more stable? Is it possible to directly convert "addresses" into a list[str]?
### 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
from langchain.tools.base import tool, BaseModel, Field
import os
from typing import List
import re
class QueryTokenAssetsInput(BaseModel):
addresses: str = Field(description="like 'address1,address2,address3',should be a list of addresses")
@tool("query_token_assets", return_direct=True, args_schema=QueryTokenAssetsInput)
def query_prices(addresses: str):
"""query the prices of tokens"""
addresses = re.findall(r'"([^"]*)', addresses)[0].split(',')
addresses = [address.replace(" ",'') for address in addresses]
return data
if __name__ == "__main__":
import json
with open("langchain/api_key.json", "r") as f:
api_key = json.load(f)
os.environ["OPENAI_API_KEY"] = api_key["OPANAI_API_KEY"]
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
agent = initialize_agent([query_prices, get_file_sizes], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
data = agent.run("the price of 0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599, 0xae7ab96520DE3A18E5e111B5EaAb095312D7fE84, 0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48")
print(data)
### Expected behavior
How can I ensure that the input for "addresses" remains stable and directly becomes a list[str] type? | How to enable precise formatting for the input of a tools utility class, such as accepting data of type list[str]? Is this achievable? | https://api.github.com/repos/langchain-ai/langchain/issues/4918/comments | 4 | 2023-05-18T10:10:20Z | 2023-10-08T07:25:51Z | https://github.com/langchain-ai/langchain/issues/4918 | 1,715,371,728 | 4,918 |
[
"langchain-ai",
"langchain"
] | ### System Info
Working on Windows machine using a conda environment.
aiohttp==3.8.4
aiosignal==1.3.1
altair==4.2.2
anyio @ file:///C:/ci_311/anyio_1676425491996/work/dist
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argon2-cffi-bindings @ file:///C:/ci_311/argon2-cffi-bindings_1676424443321/work
asttokens @ file:///opt/conda/conda-bld/asttokens_1646925590279/work
async-timeout==4.0.2
attrs @ file:///C:/ci_311/attrs_1676422272484/work
Babel @ file:///C:/ci_311/babel_1676427169844/work
backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work
backoff==2.2.1
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decorator @ file:///opt/conda/conda-bld/decorator_1643638310831/work
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Deprecated==1.2.13
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et-xmlfile==1.1.0
executing @ file:///opt/conda/conda-bld/executing_1646925071911/work
fastapi==0.95.1
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filelock==3.12.0
frozenlist==1.3.3
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ftfy==6.1.1
gitdb==4.0.10
GitPython==3.1.31
google-api-core==2.11.0
google-api-python-client==2.86.0
google-auth==2.18.0
google-auth-httplib2==0.1.0
googleapis-common-protos==1.59.0
greenlet==2.0.2
h11==0.14.0
hnswlib @ file:///D:/bld/hnswlib_1675802891722/work
httpcore==0.16.3
httplib2==0.22.0
httptools==0.5.0
httpx==0.23.3
huggingface-hub==0.14.1
idna @ file:///C:/ci_311/idna_1676424932545/work
importlib-metadata==6.6.0
ipykernel @ file:///C:/ci_311/ipykernel_1678734799670/work
ipython @ file:///C:/b/abs_d1yx5tjhli/croot/ipython_1680701887259/work
ipython-genutils @ file:///tmp/build/80754af9/ipython_genutils_1606773439826/work
ipywidgets @ file:///C:/b/abs_5awapknmz_/croot/ipywidgets_1679394824767/work
jedi @ file:///C:/ci_311/jedi_1679427407646/work
Jinja2 @ file:///C:/ci_311/jinja2_1676424968965/work
joblib==1.2.0
json5 @ file:///tmp/build/80754af9/json5_1624432770122/work
jsonschema @ file:///C:/b/abs_d40z05b6r1/croot/jsonschema_1678983446576/work
jupyter @ file:///C:/ci_311/jupyter_1678249952587/work
jupyter-console @ file:///C:/b/abs_82xaa6i2y4/croot/jupyter_console_1680000189372/work
jupyter-server @ file:///C:/ci_311/jupyter_server_1678228762759/work
jupyter_client @ file:///C:/b/abs_059idvdagk/croot/jupyter_client_1680171872444/work
jupyter_core @ file:///C:/b/abs_9d0ttho3bs/croot/jupyter_core_1679906581955/work
jupyterlab @ file:///C:/ci_311/jupyterlab_1677719392688/work
jupyterlab-pygments @ file:///tmp/build/80754af9/jupyterlab_pygments_1601490720602/work
jupyterlab-widgets @ file:///C:/b/abs_38ad427jkz/croot/jupyterlab_widgets_1679055289211/work
jupyterlab_server @ file:///C:/b/abs_e0qqsihjvl/croot/jupyterlab_server_1680792526136/work
langchain==0.0.157
lxml @ file:///C:/b/abs_c2bg6ck92l/croot/lxml_1679646459966/work
lz4==4.3.2
Markdown==3.4.3
MarkupSafe @ file:///C:/ci_311/markupsafe_1676424152318/work
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marshmallow-enum==1.5.1
matplotlib-inline @ file:///C:/ci_311/matplotlib-inline_1676425798036/work
mistune @ file:///C:/ci_311/mistune_1676425149302/work
monotonic==1.6
mpmath==1.3.0
msg-parser==1.2.0
multidict==6.0.4
mypy-extensions==1.0.0
nbclassic @ file:///C:/b/abs_c8_rs7b3zw/croot/nbclassic_1681756186106/work
nbclient @ file:///C:/ci_311/nbclient_1676425195918/work
nbconvert @ file:///C:/ci_311/nbconvert_1676425836196/work
nbformat @ file:///C:/ci_311/nbformat_1676424215945/work
nest-asyncio @ file:///C:/ci_311/nest-asyncio_1676423519896/work
networkx==3.1
nltk==3.8.1
notebook @ file:///C:/b/abs_49d8mc_lpe/croot/notebook_1681756182078/work
notebook_shim @ file:///C:/ci_311/notebook-shim_1678144850856/work
numexpr==2.8.4
numpy==1.23.5
olefile==0.46
openai==0.27.6
openapi-schema-pydantic==1.2.4
openpyxl==3.1.2
packaging @ file:///C:/b/abs_ed_kb9w6g4/croot/packaging_1678965418855/work
pandas==1.5.3
pandocfilters @ file:///opt/conda/conda-bld/pandocfilters_1643405455980/work
parso @ file:///opt/conda/conda-bld/parso_1641458642106/work
pdfminer.six==20221105
pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work
Pillow==9.5.0
platformdirs @ file:///C:/ci_311/platformdirs_1676422658103/work
ply==3.11
posthog==3.0.1
prometheus-client @ file:///C:/ci_311/prometheus_client_1679591942558/work
prompt-toolkit @ file:///C:/ci_311/prompt-toolkit_1676425940920/work
protobuf==3.20.3
psutil @ file:///C:/ci_311_rebuilds/psutil_1679005906571/work
pure-eval @ file:///opt/conda/conda-bld/pure_eval_1646925070566/work
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Pygments @ file:///opt/conda/conda-bld/pygments_1644249106324/work
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PyQt5-sip @ file:///C:/ci_311/pyqt-split_1676428895938/work/pyqt_sip
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PySocks @ file:///C:/ci_311/pysocks_1676425991111/work
python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
python-docx==0.8.11
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pywin32==305.1
pywinpty @ file:///C:/ci_311/pywinpty_1677707791185/work/target/wheels/pywinpty-2.0.10-cp311-none-win_amd64.whl
PyYAML==6.0
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QtPy @ file:///C:/ci_311/qtpy_1676432558504/work
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widgetsnbextension @ file:///C:/b/abs_882k4_4kdf/croot/widgetsnbextension_1679313880295/work
wikipedia==1.4.0
win-inet-pton @ file:///C:/ci_311/win_inet_pton_1676425458225/work
wrapt==1.14.1
XlsxWriter==3.1.0
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zipp==3.15.0
zstandard==0.21.0
### Who can help?
@vowelparrot
Hi, I facing an issue regarding llm-math tool. I am using a Tool that is created using my documents and a built-in llm tool. For some queries, llm tool receives a word problem rather than a mathematical expression. Kindly, guide how can I avoid this problem?
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
llm=OpenAI(model_name = "gpt-3.5-turbo", temperature = 0) # text-davinci-003
db = Chroma(persist_directory="db_publications",embedding_function=embedding)
chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=db.as_retriever(search_kwargs={"k": 2}))
#custom tool is created using my documents
custom_tool = [
Tool(
name="Tax_Tool",
func=chain.run,
description="Use this tool to answer tax related queries") ]
# built-in tools are loaded
tools = load_tools(["llm-math"], llm=llm)
# built-in tools are appended to the custom tool to maintain priority
for tool in tools:
custom_tool.append(tool)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(
custom_tool, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Now let's test it out!
result = agent(
{"input" : "I am married and filing jointly. How much additional Medicare Tax I have to pay if my net earnings are $300,000?"}
)
```
### Expected behavior
←[1m> Entering new AgentExecutor chain...←[0m
←[32;1m←[1;3mI need to use a tax calculator to determine the additional Medicare Tax.
Action: Calculator
Action Input: Input $300,000 and select the option for married filing jointly.←[0m2023-05-18 14:15:59.978 Uncaught app exception
Traceback (most recent call last):
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\llm_math\base.py", line 80, in _evaluate_expression
numexpr.evaluate(
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\numexpr\necompiler.py", line 817, in evaluate
_names_cache[expr_key] = getExprNames(ex, context)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\numexpr\necompiler.py", line 704, in getExprNames
ex = stringToExpression(text, {}, context)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\numexpr\necompiler.py", line 274, in stringToExpression
c = compile(s, '<expr>', 'eval', flags)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<expr>", line 1
None (this is not a math problem)
^^^^^^^^^^^^^^^^^^
SyntaxError: invalid syntax. Perhaps you forgot a comma?
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 565, in _run_script
exec(code, module.__dict__)
File "D:\Projects\TaxAdvisor\TaxAdviorWithNewDb\agent_gpt_without_observations.py", line 159, in <module>
result = agent(
^^^^^^
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\base.py", line 140, in __call__
raise e
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\base.py", line 134, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\agents\agent.py", line 922, in _call
next_step_output = self._take_next_step(
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\agents\agent.py", line 800, in _take_next_step
observation = tool.run(
^^^^^^^^^
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\tools\base.py", line 255, in run
raise e
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\tools\base.py", line 249, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\tools\base.py", line 344, in _run
self.func(
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\base.py", line 236, in run
return self(args[0], callbacks=callbacks)[self.output_keys[0]]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\base.py", line 140, in __call__
raise e
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\base.py", line 134, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\llm_math\base.py", line 149, in _call
return self._process_llm_result(llm_output, _run_manager)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\llm_math\base.py", line 103, in _process_llm_result
output = self._evaluate_expression(expression)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hizafa.nadeem\AppData\Local\anaconda3\envs\tax_app\Lib\site-packages\langchain\chains\llm_math\base.py", line 87, in _evaluate_expression
raise ValueError(
ValueError: LLMMathChain._evaluate("
None (this is not a math problem)
") raised error: invalid syntax. Perhaps you forgot a comma? (<expr>, line 1). Please try again with a valid numerical expression
| llm-math Tools takes a word problem rather than a numerical expression. | https://api.github.com/repos/langchain-ai/langchain/issues/4917/comments | 3 | 2023-05-18T09:20:29Z | 2023-12-11T20:49:37Z | https://github.com/langchain-ai/langchain/issues/4917 | 1,715,302,612 | 4,917 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
I use two agents, the same LLM, why is one Observation the output of the tool and the other not. **How to make the output of Observation the return result of TOOL**
```python
class CustomAgentExecutor(object):
def __init__(self, template: str, tools: List[BaseTool], llm):
self.template = template
self.tools = tools
self.llm = llm
self.agent_executor = self._initialize_executor()
def _initialize_executor(self):
prompt = CustomPromptTemplate(
template=self.template,
tools=self.tools,
input_variables=["input", "intermediate_steps"]
)
'''
# ONE AGENT
tool_names = [tool.name for tool in self.tools]
output_parser = CustomOutputParser(tool_names=tool_names)
llm_chain = LLMChain(llm=self.llm, prompt=prompt)
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\\nObservation:"],
allowed_tools=tool_names
)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=self.tools, verbose=True)
'''
# ANOTHER AGENT
agent_chain = initialize_agent(
self.tools,
self.llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
return agent_chain
def run(self, input_question: str):
return self.agent_executor.run(input_question)
```
```python
custom_agent_executor = CustomAgentExecutor(
template=self.template,
tools=self.tools,
llm=self.llm
)
result = custom_agent_executor.run(input_question=instruction)
```
```text
> Entering new AgentExecutor chain...
I need to figure out what month and day it is today.
Action: Time
Action Input: Current Date
Observation: 今天的日期是: 2023年05月18日, 当前的时间是: 03时29分03秒, 今天是星期四
Thought: I now know the final answer
Final Answer: 今天是五月十八号。
> Finished chain.
```
```text
> Entering new AgentExecutor chain...
Thought: 我需要知道当前的日期
Action: Time
Action Input: 当前日期
Observation: 今天是7月14号
Thought: 我现在知道最终答案
Final Answer: 今天是7月14号
> Finished chain.
```
**今天的日期是: 2023年05月18日, 当前的时间是: 03时29分03秒, 今天是星期四 【this is the return result of the tool】**
### Suggestion:
_No response_ | Issue: How to make the output of Observation the return result of TOOL | https://api.github.com/repos/langchain-ai/langchain/issues/4916/comments | 10 | 2023-05-18T07:52:21Z | 2024-02-15T16:11:50Z | https://github.com/langchain-ai/langchain/issues/4916 | 1,715,187,690 | 4,916 |
[
"langchain-ai",
"langchain"
] | ### Feature request
# Example
I have a tool that has access to my email. This tool is used by a long running agent. I'd like the tool to have long standing Read permissions but only have short lived Write permissions.
Like Githubs sudo permission, I'd like a message sent to a designated device, email, or similar, asking for authorization.
# Reality
Some of these actions maybe outside the design scope of LangChain. However, my mind goes towards a refining of the _run method within a tool. The _run method could accept an object which is responsible for getting approval or simply returning true.
As a pattern I think of dependency injection more than callbacks but wonder if that could work or it is just semantic difference.
### Motivation
Motivation is simply to secure the use of tools. After regulation of the underlying LLMs I think this is the next largest risk.
I think it talks to ~~Ben (sorry I forget his surname)~~ EDIT: got the gents name wrong, his name is Simon Wilson ( https://simonwillison.net/2023/May/2/prompt-injection-explained/) recent concerns and suggestion to have two LLMs, one freely accessible the other privileged and has input sanitized.
There is a lot of value in the thought and pattern, it feels more practical to start with permissions, especially in a "zero trust world".
### Your contribution
Happy to help in multiple ways, time based.
Immediately to discuss and document the risks, threats, and previous solutions.
Immediately to peer review MRs.
Mid-Longer term (i.e. on the back of a decent to do list) start to code a solution. | Add permission model to tools (simple sudo like at first) | https://api.github.com/repos/langchain-ai/langchain/issues/4912/comments | 5 | 2023-05-18T06:31:01Z | 2023-12-31T13:20:34Z | https://github.com/langchain-ai/langchain/issues/4912 | 1,715,084,795 | 4,912 |
[
"langchain-ai",
"langchain"
] | ### Issue you'd like to raise.
ERROR: Could not find a version that satisfies the requirement langchain==0.0.172 (from versions: 0.0.1, 0.0.2, 0.0.3, 0.0.4, 0.0.5, 0.0.6, 0.0.7, 0.0.8, 0.0.9, 0.0.10, 0.0.11, 0.0.12, 0.0.13, 0.0.14, 0.0.15, 0.0.16, 0.0.17, 0.0.18, 0.0.19, 0.0.20, 0.0.21, 0.0.22, 0.0.23, 0.0.24, 0.0.25, 0.0.26, 0.0.27, 0.0.28, 0.0.29, 0.0.30, 0.0.31, 0.0.32, 0.0.33, 0.0.34, 0.0.35, 0.0.36, 0.0.37, 0.0.38, 0.0.39, 0.0.40, 0.0.41, 0.0.42, 0.0.43, 0.0.44, 0.0.45, 0.0.46, 0.0.47, 0.0.48, 0.0.49, 0.0.50, 0.0.51, 0.0.52, 0.0.53, 0.0.54, 0.0.55, 0.0.56, 0.0.57, 0.0.58, 0.0.59, 0.0.60, 0.0.61, 0.0.63, 0.0.64, 0.0.65, 0.0.66, 0.0.67, 0.0.68, 0.0.69, 0.0.70, 0.0.71, 0.0.72, 0.0.73, 0.0.74, 0.0.75, 0.0.76, 0.0.77, 0.0.78, 0.0.79, 0.0.80, 0.0.81, 0.0.82, 0.0.83, 0.0.84, 0.0.85, 0.0.86, 0.0.87, 0.0.88, 0.0.89, 0.0.90, 0.0.91, 0.0.92, 0.0.93, 0.0.94, 0.0.95, 0.0.96, 0.0.97, 0.0.98, 0.0.99rc0, 0.0.99, 0.0.100, 0.0.101rc0, 0.0.101, 0.0.102rc0, 0.0.102, 0.0.103, 0.0.104, 0.0.105, 0.0.106, 0.0.107, 0.0.108, 0.0.109, 0.0.110, 0.0.111, 0.0.112, 0.0.113, 0.0.114, 0.0.115, 0.0.116, 0.0.117, 0.0.118, 0.0.119, 0.0.120, 0.0.121, 0.0.122, 0.0.123, 0.0.124, 0.0.125, 0.0.126, 0.0.127, 0.0.128, 0.0.129, 0.0.130, 0.0.131, 0.0.132, 0.0.133, 0.0.134, 0.0.135, 0.0.136, 0.0.137, 0.0.138, 0.0.139, 0.0.140, 0.0.141, 0.0.142, 0.0.143, 0.0.144, 0.0.145, 0.0.146, 0.0.147, 0.0.148, 0.0.149, 0.0.150, 0.0.151, 0.0.152, 0.0.153, 0.0.154, 0.0.155, 0.0.156, 0.0.157, 0.0.158, 0.0.159, 0.0.160, 0.0.161, 0.0.162, 0.0.163, 0.0.164, 0.0.165, 0.0.166)
ERROR: No matching distribution found for langchain==0.0.172
### Suggestion:
_No response_ | Issue: Could not find a version that satisfies the requirement langchain==0.0.172 | https://api.github.com/repos/langchain-ai/langchain/issues/4909/comments | 1 | 2023-05-18T06:02:43Z | 2023-05-26T10:46:41Z | https://github.com/langchain-ai/langchain/issues/4909 | 1,715,042,505 | 4,909 |
[
"langchain-ai",
"langchain"
] | ### System Info
Langchain version: latest
environment: latest Google collab
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [x] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
You can see the issue here: https://colab.research.google.com/drive/1WaW5WAHTN7xlfo_jK9AnnpjfvVSQITqe?usp=sharing
Step 0. Install `faiss-cpu`
Step 1. Load a large model that takes up most of the GPU memory
Step 2. Call FAISS.from_documents
Step 3. Error out with OutOfMemoryError: CUDA out of memory.
### Expected behavior
* We're using `faiss-cpu`, so the expectation is that all FAISS ops are on the CPU
* However, we get GPU OutOfMemoryErrors when trying to shove docs into the vector store, so 🤷 | faiss-cpu uses GPU | https://api.github.com/repos/langchain-ai/langchain/issues/4908/comments | 3 | 2023-05-18T05:29:31Z | 2024-06-07T07:35:21Z | https://github.com/langchain-ai/langchain/issues/4908 | 1,715,007,824 | 4,908 |
[
"langchain-ai",
"langchain"
] | ### Feature request
I would like to receive a prompt and depending on the prompt route to a dataframe agent or a llmchain. For example, the question may be about the aggregation of data in which I would like to utilize the dataframe agent. If the question is about pdf files I would like to use the llmchain to handle this.
### Motivation
The ability to have a universal answer flow is ideal for my scenario. I would like the ability to handle any question and respond with either a response from the dataframe or from llmchain.
### Your contribution
I am still going through the documentation and code repo itself. I can certainly work towards a PR. Want to for sure ensure that this is not already possible. Thank you! | Allow for routing between agents and llmchain | https://api.github.com/repos/langchain-ai/langchain/issues/4904/comments | 16 | 2023-05-18T04:51:16Z | 2024-04-20T16:20:33Z | https://github.com/langchain-ai/langchain/issues/4904 | 1,714,977,472 | 4,904 |
[
"langchain-ai",
"langchain"
] | ### System Info
I am using a next.js app for this
Code Used:
```
import { OpenAI } from 'langchain/llms/openai';
import { LLMChain } from "langchain/chains";
import { PromptTemplate } from "langchain/prompts";
const model = new OpenAI({
openAIApiKey: process.env.OPENAI_API_KEY,
temperature: 0, // increase temepreature to get more creative answers
modelName: 'gpt-3.5-turbo', //change this to gpt-4 if you have access
});
const prompt = PromptTemplate.fromTemplate(
template
);
const query = new LLMChain({ llm: model, prompt: prompt });
```
Using "openai": "^3.2.1",
Module parse failed: Unexpected character '' (1:0)
The module seem to be a WebAssembly module, but module is not flagged as WebAssembly module for webpack.
BREAKING CHANGE: Since webpack 5 WebAssembly is not enabled by default and flagged as experimental feature.
You need to enable one of the WebAssembly experiments via 'experiments.asyncWebAssembly: true' (based on async modules) or 'experiments.syncWebAssembly: true' (like webpack 4, deprecated).
For files that transpile to WebAssembly, make sure to set the module type in the 'module.rules' section of the config (e. g. 'type: "webassembly/async"').
(Source code omitted for this binary file)
Import trace for requested module:
../../../node_modules/@dqbd/tiktoken/tiktoken_bg.wasm
../../../node_modules/@dqbd/tiktoken/tiktoken.js
../../../node_modules/langchain/dist/base_language/count_tokens.js
../../../node_modules/langchain/dist/llms/openai.js
../../../node_modules/langchain/llms/openai.js
./app/utils/makechain.ts
./app/utils/quer.ts
./app/test/page.tsx
The code was working smoothly last night and it broke all sudden
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code Used:
```
import { OpenAI } from 'langchain/llms/openai';
import { LLMChain } from "langchain/chains";
import { PromptTemplate } from "langchain/prompts";
const model = new OpenAI({
openAIApiKey: process.env.OPENAI_API_KEY,
temperature: 0, // increase temepreature to get more creative answers
modelName: 'gpt-3.5-turbo', //change this to gpt-4 if you have access
});
const prompt = PromptTemplate.fromTemplate(
template
);
const query = new LLMChain({ llm: model, prompt: prompt });
```
### Expected behavior
The code should run smoothly and return a query
| Module parse failed: Unexpected character '' (1:0) The module seem to be a WebAssembly module, but module is not flagged as WebAssembly module for webpack | https://api.github.com/repos/langchain-ai/langchain/issues/4901/comments | 1 | 2023-05-18T04:09:27Z | 2023-05-18T05:18:59Z | https://github.com/langchain-ai/langchain/issues/4901 | 1,714,950,417 | 4,901 |
[
"langchain-ai",
"langchain"
] | ### System Info
Hi @hwchase17 @agola11
In the langchain v0.0.171 there is not feature to load 8 bit models because specifying it requires `device_map = auto` to be set which I am unable to set in the [HuggingFacePipeline](https://github.com/hwchase17/langchain/blob/master/langchain/llms/huggingface_pipeline.py)
For clarity I am trying to load 8 bit model in order to save memory and load model faster, if that's achievable
```
---> 64 task="text-generation", model_kwargs={"temperature":0, "max_new_tokens":256, "load_in_8bit": True, device_map:'auto'})
66 chain = LLMChain(llm=llm, prompt=PROMPT, output_key="nda_1", verbose=True)
68 prompt_template = """<my prompt>:
69 Context: {nda_1}
70 NDA:"""
NameError: name 'device_map' is not defined
```
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
---> 64 task="text-generation", model_kwargs={"temperature":0, "max_new_tokens":256, "load_in_8bit": True, device_map:'auto'})
66 chain = LLMChain(llm=llm, prompt=PROMPT, output_key="nda_1", verbose=True)
68 prompt_template = """<my prompt>:
69 Context: {nda_1}
70 NDA:"""
NameError: name 'device_map' is not defined
```
### Expected behavior
model must load faster | loading 8 bit models and throwing device_map = auto errors | https://api.github.com/repos/langchain-ai/langchain/issues/4900/comments | 1 | 2023-05-18T03:37:36Z | 2023-06-03T18:38:23Z | https://github.com/langchain-ai/langchain/issues/4900 | 1,714,929,586 | 4,900 |
[
"langchain-ai",
"langchain"
] | ### System Info
```
Python 3.10.4
langchain==0.0.171
redis==4.5.5
redisearch==2.1.1
```
### Who can help?
@tylerhutcherson
### 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
I was able to override #4896 by getting rid of the extra `cls` from the mentioned method. Post which, I'm getting this below error. Ideally `from_texts_return_keys` method should not be expecting `redis_url` to be passed in as a kwarg since the `redis_url` is defined during the initialization of the `Redis` class and is a mandatory argument.
```
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/vectorstores/redis.py", line 448, in from_texts
instance, _ = cls.from_texts_return_keys(
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/vectorstores/redis.py", line 389, in from_texts_return_keys
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/utils.py", line 17, in get_from_dict_or_env
return get_from_env(key, env_key, default=default)
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/utils.py", line 27, in get_from_env
raise ValueError(
ValueError: Did not find redis_url, please add an environment variable `REDIS_URL` which contains it, or pass `redis_url` as a named parameter.
```
### Expected behavior
The method `from_texts_return_keys` should not expect the `redis_url` to be passed in as a kwarg. | Redis Vectorstore: Did not find redis_url, please add an environment variable `REDIS_URL` which contains it, or pass `redis_url` as a named parameter. | https://api.github.com/repos/langchain-ai/langchain/issues/4899/comments | 5 | 2023-05-18T03:15:21Z | 2023-09-19T16:10:11Z | https://github.com/langchain-ai/langchain/issues/4899 | 1,714,917,051 | 4,899 |
[
"langchain-ai",
"langchain"
] | ### System Info
langchain="^0.0.172"
The `from langchain.callbacks import get_openai_callback` callback seems to have broken in a new release. It was working when I was on "^0.0.158". The callback is working but no token or costs are appearing.
```
2023-05-18T02:41:37.844174Z [info ] openai charges [service.openai] completion_tokens=0 prompt_tokens=0 request_id=4da70135655b48d59d7f1e7528733f61 successful_requests=1 total_cost=0.0 total_tokens=0 user=user_2PkD3ZUhCdmBHiFPNP9tPZr7OLA
```
I was experiencing another breaking change with #4717 that seems to have been resolved.
### Who can help?
@agola11 @vowelparrot
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```py
import structlog
from langchain.callbacks import get_openai_callback
log = structlog.getLogger("main")
with get_openai_callback() as cb:
result = await agent.acall({ "input": body.prompt }, return_only_outputs=True)
openai_logger.info(
"openai charges",
prompt_tokens=cb.prompt_tokens,
completion_tokens=cb.completion_tokens,
total_tokens=cb.total_tokens,
total_cost=cb.total_cost,
successful_requests=cb.successful_requests,
user=user_id
)
```
### Expected behavior
I was expecting tokens and costs to token counts to appear. | Breaking Changes | OpenAI Callback | https://api.github.com/repos/langchain-ai/langchain/issues/4897/comments | 12 | 2023-05-18T02:51:23Z | 2024-03-18T16:04:34Z | https://github.com/langchain-ai/langchain/issues/4897 | 1,714,900,925 | 4,897 |
[
"langchain-ai",
"langchain"
] | ### System Info
```
Python 3.10.4
langchain==0.0.171
redis==3.5.3
redisearch==2.1.1
```
### Who can help?
@tylerhutcherson
### 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
I was able to override issue #3893 by temporarily disabling the ` _check_redis_module_exist`, post which I'm getting the below error when calling the `from_texts_return_keys` within the `from_documents` method in Redis class. Seems the argument `cls` is not needed in the `from_texts_return_keys` method, since it is already defined as a classmethod.
```
File "/workspaces/chatdataset_backend/adapters.py", line 96, in load
vectorstore = self.rds.from_documents(documents=documents, embedding=self.embeddings)
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/vectorstores/base.py", line 296, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/vectorstores/redis.py", line 448, in from_texts
instance, _ = cls.from_texts_return_keys(
TypeError: Redis.from_texts_return_keys() got multiple values for argument 'cls'
```
### Expected behavior
Getting rid of cls argument from all the `Redis` class methods wherever required. Was able to solve the issue with this fix. | Redis Vectorstore: Redis.from_texts_return_keys() got multiple values for argument 'cls' | https://api.github.com/repos/langchain-ai/langchain/issues/4896/comments | 6 | 2023-05-18T02:46:53Z | 2023-09-22T16:09:08Z | https://github.com/langchain-ai/langchain/issues/4896 | 1,714,898,261 | 4,896 |
[
"langchain-ai",
"langchain"
] | ### System Info
- Softwares:
LangChain 0.0.171
Python 3.10.11
Jupyterlab 3.5.3
Anaconda 2.4.0
- Platforms:
Mac OSX Ventura 13.3.1
Apple M1 Max
### Who can help?
@eyurtsev please have a look on this issue. Thanks!
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
- Steps to reproduce the issue:
1. Prepare a word(docx) document that big enough(over 100MB)
2. Execute following code:
```
from langchain.document_loaders import UnstructuredWordDocumentLoader
loader = UnstructuredWordDocumentLoader("example_data/fake.docx", mode="elements")
data = loader.load()
```
- Error logs:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[53], line 1
----> 1 data = loader.load()
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/langchain/document_loaders/unstructured.py:70, in UnstructuredBaseLoader.load(self)
68 def load(self) -> List[Document]:
69 """Load file."""
---> 70 elements = self._get_elements()
71 if self.mode == "elements":
72 docs: List[Document] = list()
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/langchain/document_loaders/word_document.py:102, in UnstructuredWordDocumentLoader._get_elements(self)
99 else:
100 from unstructured.partition.docx import partition_docx
--> 102 return partition_docx(filename=self.file_path, **self.unstructured_kwargs)
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/unstructured/partition/docx.py:121, in partition_docx(filename, file, metadata_filename)
118 exactly_one(filename=filename, file=file)
120 if filename is not None:
--> 121 document = docx.Document(filename)
122 elif file is not None:
123 document = docx.Document(
124 spooled_to_bytes_io_if_needed(cast(Union[BinaryIO, SpooledTemporaryFile], file)),
125 )
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/docx/api.py:25, in Document(docx)
18 """
19 Return a |Document| object loaded from *docx*, where *docx* can be
20 either a path to a ``.docx`` file (a string) or a file-like object. If
21 *docx* is missing or ``None``, the built-in default document "template"
22 is loaded.
23 """
24 docx = _default_docx_path() if docx is None else docx
---> 25 document_part = Package.open(docx).main_document_part
26 if document_part.content_type != CT.WML_DOCUMENT_MAIN:
27 tmpl = "file '%s' is not a Word file, content type is '%s'"
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/docx/opc/package.py:128, in OpcPackage.open(cls, pkg_file)
122 @classmethod
123 def open(cls, pkg_file):
124 """
125 Return an |OpcPackage| instance loaded with the contents of
126 *pkg_file*.
127 """
--> 128 pkg_reader = PackageReader.from_file(pkg_file)
129 package = cls()
130 Unmarshaller.unmarshal(pkg_reader, package, PartFactory)
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/docx/opc/pkgreader.py:35, in PackageReader.from_file(pkg_file)
33 content_types = _ContentTypeMap.from_xml(phys_reader.content_types_xml)
34 pkg_srels = PackageReader._srels_for(phys_reader, PACKAGE_URI)
---> 35 sparts = PackageReader._load_serialized_parts(
36 phys_reader, pkg_srels, content_types
37 )
38 phys_reader.close()
39 return PackageReader(content_types, pkg_srels, sparts)
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/docx/opc/pkgreader.py:69, in PackageReader._load_serialized_parts(phys_reader, pkg_srels, content_types)
67 sparts = []
68 part_walker = PackageReader._walk_phys_parts(phys_reader, pkg_srels)
---> 69 for partname, blob, reltype, srels in part_walker:
70 content_type = content_types[partname]
71 spart = _SerializedPart(
72 partname, content_type, reltype, blob, srels
73 )
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/docx/opc/pkgreader.py:110, in PackageReader._walk_phys_parts(phys_reader, srels, visited_partnames)
106 yield (partname, blob, reltype, part_srels)
107 next_walker = PackageReader._walk_phys_parts(
108 phys_reader, part_srels, visited_partnames
109 )
--> 110 for partname, blob, reltype, srels in next_walker:
111 yield (partname, blob, reltype, srels)
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/docx/opc/pkgreader.py:105, in PackageReader._walk_phys_parts(phys_reader, srels, visited_partnames)
103 reltype = srel.reltype
104 part_srels = PackageReader._srels_for(phys_reader, partname)
--> 105 blob = phys_reader.blob_for(partname)
106 yield (partname, blob, reltype, part_srels)
107 next_walker = PackageReader._walk_phys_parts(
108 phys_reader, part_srels, visited_partnames
109 )
File ~/anaconda3/envs/ForLangChain/lib/python3.10/site-packages/docx/opc/phys_pkg.py:108, in _ZipPkgReader.blob_for(self, pack_uri)
103 def blob_for(self, pack_uri):
104 """
105 Return blob corresponding to *pack_uri*. Raises |ValueError| if no
106 matching member is present in zip archive.
107 """
--> 108 return self._zipf.read(pack_uri.membername)
File ~/anaconda3/envs/ForLangChain/lib/python3.10/zipfile.py:1477, in ZipFile.read(self, name, pwd)
1475 def read(self, name, pwd=None):
1476 """Return file bytes for name."""
-> 1477 with self.open(name, "r", pwd) as fp:
1478 return fp.read()
File ~/anaconda3/envs/ForLangChain/lib/python3.10/zipfile.py:1516, in ZipFile.open(self, name, mode, pwd, force_zip64)
1513 zinfo._compresslevel = self.compresslevel
1514 else:
1515 # Get info object for name
-> 1516 zinfo = self.getinfo(name)
1518 if mode == 'w':
1519 return self._open_to_write(zinfo, force_zip64=force_zip64)
File ~/anaconda3/envs/ForLangChain/lib/python3.10/zipfile.py:1443, in ZipFile.getinfo(self, name)
1441 info = self.NameToInfo.get(name)
1442 if info is None:
-> 1443 raise KeyError(
1444 'There is no item named %r in the archive' % name)
1446 return info
KeyError: "There is no item named 'NULL' in the archive"
```
### Expected behavior
Loading a big size Word document should be OK without any issue. | Failed to load Word document over 100MB. | https://api.github.com/repos/langchain-ai/langchain/issues/4894/comments | 1 | 2023-05-18T01:39:56Z | 2023-09-10T16:15:57Z | https://github.com/langchain-ai/langchain/issues/4894 | 1,714,857,116 | 4,894 |
[
"langchain-ai",
"langchain"
] | ### System Info
Langchain version: 0.0.172
Platform: Linux (Ubuntu)
### Who can help?
@vowelparrot
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When trying to use a python agent for simple queries, the agent often does not recognize Python REPL as a valid tool:
```
> Entering new AgentExecutor chain...
I can write a function to generate the nth fibonacci number and then call it with n=4.
Action: [Python REPL]
Action Input:
def fibonacci(n):
if n <= 1:
return n
else:
return fibonacci(n-1) + fibonacci(n-2)
print(fibonacci(4))
Observation: [Python REPL] is not a valid tool, try another one.
```
My instantiation of the agent:
```
custom_prefix = PREFIX + "When writing a code block, do not include the word 'python' after the first three ticks. You must graph your findings and save a .png of the graph on the local file system at [a path on my local machine]. The corpus consists of .txt files at this directory: [another path on my machine]."
python_agent = create_python_agent(llm=llm, tool=PythonREPLTool(), verbose=True, max_tokens=1000, prefix=custom_prefix)
python_agent("What are the top 10 bi-grams in the corpus? Only parse the .txt files. Translate the final n-grams to English for the chart.")
```
Model then gets stuck in a loop trying to use Python REPL. I included instructions in `custom_prefix` because the model repeatedly got this error, too:
```
> Entering new AgentExecutor chain...
I need to read in all the .txt files in the corpus and tokenize them into bi-grams. Then I need to count the frequency of each bi-gram and return the top 10.
Action: Python REPL
Action Input:
```python
import os
import nltk
from collections import Counter
from nltk import word_tokenize
from nltk.util import ngrams
corpus_dir = a directory on my machine
files = [os.path.join(corpus_dir, f) for f in os.listdir(corpus_dir) if f.endswith('.txt')]
bi_grams = []
for file in files:
with open(file, 'r') as f:
text = f.read()
tokens = word_tokenize(text)
bi_grams += ngrams(tokens, 2)
bi_gram_freq = Counter(bi_grams)
top_10_bi_grams = bi_gram_freq.most_common(10)
...
print(top_10_bi_grams)
```NameError("name 'python' is not defined")
```
### Expected behavior
I expect the Python agent to recognize PythonREPL as a valid tool. In fact, sometimes it does! But more often than not, it does not recognize PythonREPL as a tool. The query I included in the above code snippet worked maybe once in every 50 tries. | PythonREPL agent toolkit does not recognize PythonREPL as a valid tool | https://api.github.com/repos/langchain-ai/langchain/issues/4889/comments | 10 | 2023-05-17T23:26:11Z | 2024-06-06T13:04:03Z | https://github.com/langchain-ai/langchain/issues/4889 | 1,714,774,587 | 4,889 |
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