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| 1,985,658,707 |
I_kwDOIPDwls52Wr9T
| 13,128 |
How do I use multiple GPU when using a model with hugging face?
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[] | null | 7 | 2023-11-09T13:45:39 | 2023-12-22T09:51:55 | null |
NONE
| null |
### Issue you'd like to raise.
Hi.
I am currently using the model of the hugging face.
The code I use is below.
`llm = HuggingFacePipeline.from_model_id(model_id="beomi/llama-2-ko-7b", task="text-generation", model_kwargs={"max_length": 2000}, device=0)`
Currently I am only using device=0, but I would like to load the model using multiple devices (device=0,1,2,3).
But I couldn't find a way to handle multiple devices for HuggingFacePipeline.from_model_id.
If you know anything about this, I hope you can help.
### Suggestion:
_No response_
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Streaming not working when using RunnableLambda
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[] | null | 3 | 2023-11-09T13:32:54 | 2023-11-09T14:54:59 | 2023-11-09T14:54:58 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.332
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The default `Runnable#stream` doesn't stream, but rather defers to `invoke`. As `RunnableLambda` doesn't override the `stream` function, this causes the entire chain to run with `invoke` instead of `stream` when it is used in any part of the chain.
```python
chain = RunnableLambda(lambda x: x) | ChatOpenAI()
for part in chain.stream("hello"):
print(part)
```
### Expected behavior
What I'd expect is that I could use a `LambdaRunnable` to transform my inputs on-the-fly, then send it through to the rest of my chain and still use the stream/batch/etc functionalities that LCEL should bring.
If the early runnables don't support streaming, that doesn't mean that the final output can't be streamed. Currently when any of the runnables in a chain don't support streaming, the entire chain doesn't support streaming.
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I_kwDOIPDwls52WZbk
| 13,125 |
Custom implementation for query checker tool
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[] | null | 10 | 2023-11-09T13:05:16 | 2023-11-10T14:56:11 | null |
NONE
| null |
### Feature request
We are using langchain create_sql_agent to build a chat engine with database.
I want to write custom implementation for tool : query_sql_checker_tool
In this custom logic, we want to modify the sql query before execution.
Example:
original query : select * from transaction where type = IPC
modified query : select * from (select * from transaction where account_id = 123) where type = IPC
Can you please provide me way forward to achieve this ?
### Motivation
To protect some data from client, need this functionality
### Your contribution
I'm not familiar with the internal code, but I can help if guided.
|
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| 13,124 |
Adding timeout to ChatOpenAI raises ConfigError
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[] | null | 4 | 2023-11-09T12:47:32 | 2023-11-13T09:49:28 | 2023-11-13T09:49:28 |
NONE
| null |
### System Info
Running langchain==0.0.332 with python 3.11 and openai==1.2.0 on Windows.
### 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
```python
from langchain.chat_models import ChatOpenAI
ChatOpenAI(
model="gpt-3.5-turbo",
request_timeout=5,
)
```
### Expected behavior
Should run without errors.
Likely due to newly introduced `httpx.Timeout` type in `request_timeout` (https://github.com/langchain-ai/langchain/pull/12948). Always importing httpx and tiktoken (i.e. not conditionally on `TYPE_CHECKING`) fixes the issue.
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PR_kwDOIPDwls5fBvUJ
| 13,123 |
Expose chat loaders objects in top-level package
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[] | null | 2 | 2023-11-09T12:06:35 | 2023-12-16T09:54:53 | 2023-12-16T09:54:46 |
CONTRIBUTOR
| null |
- **Description:** the change imports chat loader classes and utilities to the `__init__` of the chat_loaders module similarly to what is done for the other modules.
- **Twitter handle:** cbornet_
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| 13,122 |
httpx openai support
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[] | null | 3 | 2023-11-09T12:04:56 | 2023-11-19T10:40:16 | null |
NONE
| null |
### Feature request
please support httpx_client in openai version 1.1.1
just one more parameter
if there is another solution i would like to get it
thanks
### Motivation
cant work without ssl cert
### Your contribution
in AzureChatOpenAI and AzureOpenAI class
add parameter openai_http_client
in method validate_environment
please add at line 134 the next code
values["http_client"] = get_from_dict_or_env(
values, "openai_http_client", "OPENAI_HTTP_CLIENT", default=""
)
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I_kwDOIPDwls52VZnR
| 13,120 |
ModuleNotFoundError: No module named 'langchain.server'
|
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open
| false | null |
[] | null | 3 | 2023-11-09T10:28:35 | 2023-11-09T10:43:53 | null |
NONE
| null |
### System Info
Ubuntu 22.04
langchain 0.0.332
python 3.10
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
pip install langchain --upgrade
langchain-server
### Expected behavior
Traceback (most recent call last):
File "/home/ps/anaconda3/envs/langchain/bin/langchain-server", line 6, in <module>
from langchain.server import main
ModuleNotFoundError: No module named 'langchain.server'
|
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| 1,985,293,295 |
I_kwDOIPDwls52VSvv
| 13,119 |
LCEL stream function doesn't use LLM cache
|
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[] | null | 4 | 2023-11-09T10:12:40 | 2023-12-01T10:07:12 | null |
CONTRIBUTOR
| null |
### System Info
LangChain version : 0.0.327
Python version : 3.10.11
Platform : Windows 11
### Who can help?
@agola11 @hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
I was using streaming callbacks with LLMChains.
I switched now to use LangChain Expression Language (LCEL), and using the stream/astream functions.
They don't seem to save the results to the configured LLM cache and don't load the results from there.
I'm not sure if this is intentional, or a bug.
If it is intentional I'm wondering how I should use the cache.
Code to reproduce the issue:
```python
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.globals import set_llm_cache
from langchain.cache import InMemoryCache
set_llm_cache(InMemoryCache())
model = ChatOpenAI(cache=True)
prompt = ChatPromptTemplate.from_template(
"tell me a joke in about 5 sentences about {topic}"
)
chain = prompt | model
for s in chain.stream({"topic": "bears"}):
print(s.content, end="", flush=True)
print("\n\nFINISHED FIRST COMPLETION\n")
for s in chain.stream({"topic": "bears"}):
print(s.content, end="", flush=True)
```
### Expected behavior
The second result should be the same and should be retrieved quickly from the LLM cache.
|
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| 1,985,272,196 |
PR_kwDOIPDwls5fBA5W
| 13,118 |
(Astra DB/Cassandra) Minor clarification about dependencies in the demo notebook
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[] | null | 1 | 2023-11-09T10:01:23 | 2023-11-09T14:26:42 | 2023-11-09T14:19:16 |
CONTRIBUTOR
| null |
This PR helps developers trying the Astra DB / Cassandra vector store quickstart notebook by making it clear what other dependencies are required.
|
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| 1,985,253,003 |
I_kwDOIPDwls52VI6L
| 13,117 |
Fusion_RAG + Semi_Structured_RAG?
|
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[] | null | 1 | 2023-11-09T09:53:44 | 2023-11-09T09:54:14 | null |
NONE
| null |
### Feature request
Langchain is a great work!
Is there any possible that you guys can combine [Fusion](https://github.com/run-llama/llama_index/blob/main/docs/examples/low_level/fusion_retriever.ipynb) and [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)?
### Motivation
There are a lot of tables and text in my data. First of all, I have tried Fusion_RAG, which is much better than the baseline, but it is limited to the text, and the table can not be processed, so I wondered if there a way could combine Semi_Structured_RAG and Fusion_RAG so that I could deal with both text and table at the same time. ^-^
### Your contribution
Please make the fusion: BM25+Vec+Table
|
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I_kwDOIPDwls52VDmv
| 13,116 |
Qianfan llm error calling "ERNIE-Bot-4"
|
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[] | null | 3 | 2023-11-09T09:42:52 | 2023-11-10T03:54:08 | null |
NONE
| null |
### System Info
langchain version 0.0.332, affect all platforms
There is a mistake in the file:
qianfan\resources\llm\completion.py
this `endpoint="/chat/completions-pro`, is not correct ,it should be `endpoint="/chat/completions_pro`,"
like below:
``
"ERNIE-Bot-4": QfLLMInfo(
endpoint="/chat/completions_pro",
required_keys={"messages"},
optional_keys={
"stream",
"temperature",
"top_p",
"penalty_score",
"user_id",
"system",
},
),
``
### 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
llm = QianfanLLMEndpoint(streaming = True,temperature = 0.5)
# llm.model = "ERNIE-Bot-turbo"
llm.model = "ERNIE-Bot-4"
# llm.model = "ChatGLM2-6B-32K"
res = llm("hi")
this will report error
### Expected behavior
should work without error
|
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| 1,985,225,113 |
I_kwDOIPDwls52VCGZ
| 13,115 |
langchain.graph RDFGraph does not read .owl extension files
|
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[] | null | 3 | 2023-11-09T09:39:19 | 2023-11-09T09:56:03 | 2023-11-09T09:56:03 |
NONE
| null |
### System Info
Langchain version: 0.0.332
Python version: 3.11.5
### Who can help?
@hwchase17
When loading a local .owl file (the standard example pizza.owl) the operation breaks and gives the following error for all the URI:
does not look like a valid URI, trying to serialize this will break.
Here's the traceback
```
Traceback (most recent call last):
File ~\AppData\Roaming\Python\Python311\site-packages\IPython\core\interactiveshell.py:3526 in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
Cell In[13], line 4
graph = RdfGraph(
File C:\Python311\Lib\site-packages\langchain\graphs\rdf_graph.py:159 in __init__
self.graph.parse(source_file, format=self.serialization)
File C:\Python311\Lib\site-packages\rdflib\graph.py:1501 in parse
raise se
File C:\Python311\Lib\site-packages\rdflib\graph.py:1492 in parse
parser.parse(source, self, **args)
File C:\Python311\Lib\site-packages\rdflib\plugins\parsers\notation3.py:2021 in parse
p.loadStream(stream)
File C:\Python311\Lib\site-packages\rdflib\plugins\parsers\notation3.py:479 in loadStream
return self.loadBuf(stream.read()) # Not ideal
File C:\Python311\Lib\site-packages\rdflib\plugins\parsers\notation3.py:485 in loadBuf
self.feed(buf)
File C:\Python311\Lib\site-packages\rdflib\plugins\parsers\notation3.py:511 in feed
i = self.directiveOrStatement(s, j)
File C:\Python311\Lib\site-packages\rdflib\plugins\parsers\notation3.py:532 in directiveOrStatement
return self.checkDot(argstr, j)
File C:\Python311\Lib\site-packages\rdflib\plugins\parsers\notation3.py:1214 in checkDot
self.BadSyntax(argstr, j, "expected '.' or '}' or ']' at end of statement")
File C:\Python311\Lib\site-packages\rdflib\plugins\parsers\notation3.py:1730 in BadSyntax
raise BadSyntax(self._thisDoc, self.lines, argstr, i, msg)
File <string>
BadSyntax
```
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behaviour:
1. Get the source file from : https://protege.stanford.edu/ontologies/pizza/pizza.owl and place it where the code runs
2. Use the following code:
```
from langchain.chains import GraphSparqlQAChain
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="pizza.owl",
standard="owl"
)
graph.load_schema()
print(graph.get_schema)
```
### Expected behavior
For the graph to load and for graph.get_schema to show the classes and object properties.
|
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| 1,985,218,334 |
I_kwDOIPDwls52VAce
| 13,114 |
Issue: <ValidationError: 1 validation error for ChatOpenAI __root__ `openai` has no `ChatCompletion` attribute, this is likely due to an old version of the openai package. Try upgrading it with `pip install --upgrade openai`. (type=value_error)>
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[] | null | 6 | 2023-11-09T09:35:22 | 2023-11-09T23:09:05 | null |
NONE
| null |
# Issue you'd like to raise.
## Issue: <ValidationError: 1 validation error for ChatOpenAI __root__ openai has no ChatCompletion attribute, this is likely due to an old version of the openai package. Try upgrading it with pip install --upgrade openai. (type=value_error)>
### Import necessary packages for Streamlit app, PDF processing, and OpenAI integration.
```
`import` streamlit as st
import pdfplumber
import os
from langchain.vectorstores import faiss
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain.docstore.document import Document
from dotenv import load_dotenv
```
### Load and verify environment variables, specifically the OpenAI API key.
### Load .env file that is in the same directory as your script.
```
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if OPENAI_API_KEY is None:
raise ValueError("OpenAI API key not found. Make sure you have an .env file with the key defined.")
```
### Initialize the OpenAI language model with a given API key and temperature setting.
```
chain = ConversationalRetrievalChain.from_llm(
llm = ChatOpenAI(temperature=0.1,model_name='gpt-4'),
retriever=vectorstore.as_retriever())
```
### Initializing the Embedding Model and OpenAI Model
```
embeddings = OpenAIEmbeddings()
vectorstore = faiss.from_documents(data, embeddings)
```
### Process an uploaded PDF document and extract its text content.
```
def process_document(uploaded_file):
with pdfplumber.open(uploaded_file) as pdf:
document_text = "\n".join(page.extract_text() for page in pdf.pages if page.extract_text())
return document_text
```
### Summarize the extracted text from the document using the OpenAI language model.
```
def summarize_document(llm, document_text):
text_splitter = CharacterTextSplitter(max_length=1000)
texts = text_splitter.split_text(document_text)
docs = [Document(content=t) for t in texts]
summarize_chain = load_summarize_chain(llm, chain_type='map_reduce')
return summarize_chain.run(docs)
```
### Initialize a conversation chain with memory capabilities for the chatbot.
```
def` initialize_conversation_chain(llm):
return ConversationalRetrievalChain(
llm=llm,
memory=ConversationBufferWindowMemory(k=5) # Stores the last 5 interactions.
)
```
### Define the main function to run the Streamlit application.
```
def run_app():
llm = initialize_llm(OPENAI_API_KEY)
st.title("Earnings Call Analysis App")
### UI for document upload and processing.
uploaded_file = st.file_uploader("Upload your earnings call transcript", type=["pdf"])
process_button = st.button("Process Document")
```
### Process document and generate summaries
```
if process_button and uploaded_file:
with st.spinner('Processing Document...'):
document_text = process_document(uploaded_file)
summaries = summarize_document(llm, document_text)
display_summaries(summaries)
st.success("Document processed!")
```
### UI for interactive chatbot with memory feature.
```
conversation_chain = initialize_conversation_chain(llm)
user_input = st.text_input("Ask a question about the earnings call:")
if st.button('Get Response'):
with st.spinner('Generating response...'):
response = generate_chat_response(conversation_chain, user_input, document_text)
st.write(response)
```
### Display summaries on the app interface and provide download option for each.
```
def display_summaries(summaries):
if summaries:
for i, summary in enumerate(summaries):
st.subheader(f"Topic {i+1}")
st.write("One-line topic descriptor: ", summary.get("one_line_summary", ""))
st.write("Detailed bulleted topic summaries: ", summary.get("bulleted_summary", ""))
download_summary(summary.get("bulleted_summary", ""), i+1)
```
### Create a downloadable summary file.
```
def download_summary(summary, topic_number):
summary_filename = f"topic_{topic_number}_summary.txt"
st.download_button(
label=f"Download Topic {topic_number} Summary",
data=summary,
file_name=summary_filename,
mime="text/plain"
)
```
### Generate a response from the chatbot based on the user's input and document's context.
```
def generate_chat_response(conversation_chain, user_input, document_text):
response = conversation_chain.generate_response(
prompt=user_input,
context=document_text
)
return response.get('text', "Sorry, I couldn't generate a response.")
if __name__ == "__main__":
run_app()
```
### Suggestion:
_No response_
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| 1,984,928,992 |
I_kwDOIPDwls52T5zg
| 13,112 |
How can we selectively pass memory to specific tools without passing it to all tools?
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[] | null | 3 | 2023-11-09T06:33:12 | 2023-11-17T06:20:39 | null |
NONE
| null |
### Issue you'd like to raise.
for example in my below case
```python
english_tools = [
Tool(name="SomeNAME_1",
func=lambda q: app.finance_chain.run(q),
description=" Some app related description ",
return_direct=True,
coroutine=lambda q: app.finance_chain.arun(q),
),
Tool(name="SomeNAME_2",
func=lambda q: app.rqa(q),
description=" Some app related description ",
coroutine=lambda q: app.rqa_english.arun(q),
return_direct=True
),
Tool.from_function(
name="SomeNAME_3",
func=lambda q: app.pd_agent(q),
description=" Some app related description",
coroutine=lambda q: app.pd_agent.arun(q),
)
]```
so when SomeNAME_3 invoke i don't wann pass memory to this tool
### Suggestion:
_No response_
|
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| 1,984,927,783 |
PR_kwDOIPDwls5e_1xQ
| 13,111 |
chore: bump momento dependency version and refactor search hit usage
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[] | null | 3 | 2023-11-09T06:32:12 | 2023-11-13T17:13:40 | 2023-11-13T17:12:21 |
CONTRIBUTOR
| null |
**Description**
Bumps the Momento dependency to the latest version and refactors the usage of `SearchHit` in the Momento Vector Index (MVI) vector store integration. This change is a one liner where we use the preferred attribute `score` to read the query-document similarity instead of `distance`. The latest versions of Momento clients will use this attribute going forward.
**Dependencies**
Updated the Momento dependency to latest version.
**Tests**
💚 I re-ran the existing MVI integration tests (`tests/integration_tests/vectorstores/test_momento_vector_index.py`) and they pass.
**Review**
cc @baskaryan @eyurtsev
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PR_kwDOIPDwls5e_qxg
| 13,110 |
Bagatur/oai tool
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[] | null | 1 | 2023-11-09T05:42:12 | 2023-11-09T15:29:30 | 2023-11-09T15:29:29 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5e_fUb
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feat: FastEmbed embedding provider
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CONTRIBUTOR
| null |
## Description:
This PR intends to add [Qdrant/FastEmbed](https://qdrant.github.io/fastembed/) as a local embeddings provider, associated tests and documentation.
**Documentation preview:** https://langchain-git-fork-anush008-master-langchain.vercel.app/docs/integrations/text_embedding/fastembed
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I_kwDOIPDwls52Ta63
| 13,108 |
Summarization chain batching is not working with Replicate
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[] | null | 3 | 2023-11-09T04:33:37 | 2023-11-16T00:07:35 | null |
NONE
| null |
### System Info
LangChain version: 0.0.300
Python version: 3.10.12
### 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
Steps to reproduce:
```py
# Prompt
prompt_text = """You are an assistant tasked with summarizing tables and text. \
Give a concise summary of the table or text. Table or text chunk: {element} """
prompt = PromptTemplate.from_template(prompt_text)
# Summary chain
model = Replicate(
model="meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0",
model_kwargs={"temperature": 0.75, "max_length": 3000, "top_p":0.25}
)
summarize_chain = {"element": lambda x: x} | prompt | model | StrOutputParser()
# Apply to text
texts = [i.text for i in text_elements]
text_summaries = summarize_chain.batch(texts, {"max_concurrency": 5})
```
Error:
```txt
ValueError: config must be a list of the same length as inputs, but got 27 configs for 5 inputs
```
With OpenAI, it is working as expected.
### Expected behavior
The summarization chain (powered by Replicate) should process each chunks batch-wise.
|
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I_kwDOIPDwls52TQMX
| 13,107 |
Performing a Graph SPARQL Query with a Local LLM
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[] | null | 5 | 2023-11-09T03:37:33 | 2023-11-20T13:21:36 | null |
NONE
| null |
Hello all, I'm attempting to perform a SPARQL graph query using my local LLM, but it appears that something is amiss. Please feel free to share any helpful tips or guidance.
`graph = RdfGraph(
source_file="http://www.w3.org/People/Berners-Lee/card",
standard="rdf",
local_copy="test1109.ttl",
)
tokenizer = AutoTokenizer.from_pretrained('C:\\data\\llm\\chatglm-6b-int4', trust_remote_code=True)
model = AutoModel.from_pretrained('C:\\data\\llm\\chatglm-6b-int4', trust_remote_code=True).half().cuda().eval()
chain = GraphSparqlQAChain.from_llm(model, graph=graph, verbose=True)
question = "What is Tim Berners-Lee's work homepage?"
result = chain.run(question)`
` File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 2 validation errors for LLMChain
llm
instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)
llm
instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)`
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Vectara rag mq
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CONTRIBUTOR
| null |
- **Description:** another Vectara template for MultiQuery RAG flow
- **Twitter handle:** @ofermend
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How to use a different prompt template upon runnable retry?
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[] | null | 2 | 2023-11-09T03:13:18 | 2023-11-09T03:26:15 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Here's an example implementation of `RunnableRetry`,
```
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.schame.runnable.retry import RunnableRetry
template = PromptTemplate.from_template("tell me a joke about {topic}.")
error_template = PromptTemplate.from_template("tell me a joke about {topic} with {context}.")
model = ChatOpenAI(temperature=0.5)
chain = template | model
retryable_chain = RunnableRetry(bound=chain, max_attempt_number=3, callback={'prompt': error_template})
```
As for now `RunnableRetry` won't support callback and I'm not sure about altering the first step of the RunnableSequence with modified inputs (inputs along with the error message.)
### Suggestion:
_No response_
|
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Tool Retrieval Template
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[] | null | 1 | 2023-11-09T02:17:25 | 2023-11-09T02:33:32 | 2023-11-09T02:33:31 |
COLLABORATOR
| null |
Adds a template like https://python.langchain.com/docs/modules/agents/how_to/custom_agent_with_tool_retrieval
Uses OpenAI functions, LCEL, and FAISS
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PR_kwDOIPDwls5e-74d
| 13,102 |
rfc: support hybrid in pinecone
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[] | null | 1 | 2023-11-09T01:27:18 | 2024-01-13T01:46:51 | 2024-01-13T01:46:51 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls52S2os
| 13,101 |
Structured Output Parser Always Gives Error
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[] | null | 6 | 2023-11-09T01:22:50 | 2023-11-09T18:57:26 | null |
NONE
| null |
### System Info
langchain==0.0.330
openai==0.28.1
python==3.9.17
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have written a simple structured output parser. I am using to extract useful data from a document text. Here's my code:
```
import os
import logging
from dotenv import load_dotenv
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
load_dotenv()
response_schemas = [
ResponseSchema(
name="document_type", description="Type of document, typically found on top"
),
ResponseSchema(name="shipper", description="Shipper name found in the data"),
ResponseSchema(name="consignee", description="Consignee name found in the data"),
ResponseSchema(name="point_of_origin", description="Point of origin in the data"),
ResponseSchema(
name="customer_order_number", description="Customer order number in the data"
),
ResponseSchema(
name="order_number", description="Order number mentioned in the data"
),
ResponseSchema(
name="bill_of_lading",
description="Bill of lading number(B/L number) found in the data",
),
ResponseSchema(
name="carrier_name", description="Carrier name mentioned in the data"
),
ResponseSchema(
name="required_ship_date", description="Required ship date in date format"
),
ResponseSchema(
name="shipped_date", description="Shipped date, typically separated by /"
),
ResponseSchema(
name="transportation_mode", description="Transportation mode such as truck etc."
),
ResponseSchema(
name="vehicle_number", description="Vehicle number found in the data"
),
ResponseSchema(name="routing_info", description="Routing info found in the data"),
ResponseSchema(
name="invoice_to_buyer", description="Invoice to buyer data found in the data"
),
ResponseSchema(
name="consignee_number", description="Consignee number mentioned in the data"
),
ResponseSchema(
name="net_weight",
description="Net weight found in the data, typically found on the second page. It's a number succeeded by weight symbol such as kg/lb/1b/15/16 and ends with NT (Net weight).",
),
ResponseSchema(name="ticket_number", description="Ticket number found in the data"),
ResponseSchema(name="outbound_date", description="Outbound date found in the data"),
]
system_prompt = """
Following is the data extracted from a document through OCR wrapped inside <ocr_data> delimeter. It may be unstructured and unorganized, and you'll help me extract key information from this data. The data can be nuanced, and field and it's respective values may be at different positions. The presented data can be of multiple pages, separated by (------). Analyze the OCR data below and give me the value of given fields. If you can't find the values in the OCR data, simply return 'N/A'.
"""
class BolAgent:
def __init__(self):
self.openai_api_key = os.getenv("OPENAI_API_KEY")
self.llm = OpenAI(
openai_api_key=self.openai_api_key, temperature=0.1, max_tokens=1000
)
self.chat_model = ChatOpenAI(
model="gpt-3.5-turbo-16k",
openai_api_key=self.openai_api_key,
temperature=0,
)
self.response_schemas = response_schemas
self.system_prompt = system_prompt
def extract_paramerts(
self,
ocr_data,
):
output_parser = StructuredOutputParser.from_response_schemas(
self.response_schemas
)
input_data = f"<ocr_data>/n{ocr_data}/n</ocr_data>"
format_instructions = output_parser.get_format_instructions()
prompt = ChatPromptTemplate(
messages=[
HumanMessagePromptTemplate.from_template(
"{system_prompt}\n\n{format_instructions}\n\n{input_data}"
)
],
input_variables=["system_prompt", "input_data"],
partial_variables={"format_instructions": format_instructions},
)
llm_input = prompt.format_prompt(
system_prompt=system_prompt, input_data=input_data
)
logging.info(f"LLM Input: {llm_input}")
output = self.chat_model(llm_input.to_messages())
logging.info(f"LLM Output: {output}")
result = output_parser.parse(output.content)
return result
```
When I use this code on any input data, the output parser gives error most of the time. Here's a sample input data:
`\n------------------\nSTRAIGHT BILL OF LADING - SHORT FORM\nTEST\nCHEMTRADE\nFICHE D\'EXPEDITION - FORMULE REGULIERE\nS4D\nSHIPPER/EXPEDITEUR\nChemtrade West Limited Partnership\nTIME IN/ARRIVEE\nGROSS/BRUT\nCONSIGNEE/DESTINATAIRE\nSASK POWER\n TARE\nSHIP TO/EXPEDIEZ A\nCORY \nCOGENERATION STATION\nTIME OUT/DEPART\n8 KM W OF SASKATOON HWY 7\nNET\nVANSCOY SOK 1VO SK CA\nPOINT OF ORIGIN/POINT D\'EXPEDITION\nCUSTOMER ORDER NO./N DE COMMANDE DU CLIENT\nORDER NO./N DE COMM.\n3/L NO./NDE CONN.\nCHEMTRADE (SASKATOON)\nS\n1856\n80001877\n CARRIER NAME/NOM DU TRANSPORTEUR\nREQUIRED SHIP DATE/DATE EXP.DEM.\nDATE SHIPPED/EXPEDIE LE\nCARON TRANSPORT LTD\nNov 06,2023\nTRANSPORTATION MODE/MODE DE TRANSPORT\nVEHICLE T/C NO. - MARQUE DU WAGON\nTruck\n UNIVAR CANADA LTD.\n ROUTING/ITINERAIRE\nCONSIGNEE#/CONSIGNATAIRE\nPAGE\n600929\n1 of\n3\nNO.AND DESCRIPTION OF PACKS\nD.G.\nDESCRIPTION OF ARTICLES AND SPECIAL MARKS\nNET WEIGHT KG\nNBRE ET DESCRIPTION DE COLIS\nDESCRIPTION DES ARTICLES ET INDICATIONS SPECIALS\nPOIDS NET\n1 TT\nX\nUN1830, SULFURIC ACID, 8, PG II\n21.000 Tonne\nSULFURIC ACID 93%\nER GUIDE #137\n4 PLACARDS REQUIRED; CLASS 8, CORROSIVE\nSTCC 4930040\nSulfuric Acid 93%\nCOA W/ SHIPMENT\nDELIVERY HOURS: 8:OOAM-1: OOPM MON-THURS\nATTENDANCE DURING OFFLOAD REQUIRED\nSAFETY GOGGLES, FACE SHIELD, GLOVES, BOOTS, HARD\nHAT, STEEL TOED SHOES, PROTECTIVE SUIT\n3" QUICK CONNECT CAMLOCK; 1 HOSE REQUIRED\nPersonal Protective Equipment: Gloves. Protective clothing. Protective goggles. Face shield.\nnsufficient ventilation: wear respiratory protection.\nERP 2-1564 and Chemtrade Logistics 24-Hour Number >>\n1-866-416-4404\nPIU 2-1564 et Chemtrade Logistics Numero de 24 heures >>\n1-866-416-4404\nConsignor / Expediteur:\nLocation / Endroit:\nCHEMTRADE WEST LIMITED PARTNERSHIP\n11TH STREET WEST\nI hereby declare that the contents of this consignment are fully and accurately described above by the proper shipping\nSASKATOON SK CA\nare in all respects in proper condition for transport according to the Transportation of Dangerous Goods Regulations.\nS7K 4C8\nPer/Par:Michael Rumble, EHS Director, Risk Management\nIF CHARGES ARE TO BE PREPAID, WRITE OR STAMP\nJe declare que le contenu de ce chargement est decrit ci-dessus de faconcomplete et exacte par Iappellation reglementaire\nINDIQUER ICI SI L\'ENVOI SE FAIT EN "PORT-PAYE"\negards bien conditionne pouretre transporte conformement au Reglement sur le transport des marchandises dangereuses.\nPrepaid\nFORWARD INVOICE FOR PREPAID FREIGHT\nChemtrade West Limited Partnership\nQUOTING OUR B/L NO.TO:\n155 Gordon\nBaker Rd #300\nWeight Agreement\nFAIRE SUIVRE FACTURE POUR EXPEDITION PORT\nToronto,\nOnt.\nM2H 3N5\nPAYE EN REFERANT A NOTRE NUMERO DE CONN.A:\nSHIPPER\nChemtrade West Limited\nAGENT\nCONSIGNEE.\nEXPEDITEUR\nPartnership\nDESTINATAIRE\nPER\nPERMANENT POST OFFICE ADDRESS OF SHIPPER\nPER\nPER\nPAR\n(ADRESSE POSTALE PERMANENTE DE L\'EXPEDITEUR)\nTHESE PRODUCTS ARE SOLD AND SHIPPED IN ACCORDANCE WITH\nTHE TERMS OF SALES ON THE REVERSE SIDE OF THIS,DOCUMENT.\nResponsible Care\nCES PRODUITS SONT VENDUS ET EXPEDIES CONFORMEMENTAUX\nCONDITIONS DE VENTE APPARAISSANT AU VERSO DE LA PRESENTE\nOur commitment to sustainability.\nS4D PRASRNov 06,2023 1618`
Upon further debugging I found that for some reason the output has two triple-backticks at the end and because of this the Structured Output Parser ends up giving the error. Here the output for better clarity (Notice the end of output):
`content='```json\n{\n\t"document_type": "STRAIGHT BILL OF LADING - SHORT FORM",\n\t"shipper": "Chemtrade West Limited Partnership",\n\t"consignee": "SASK POWER",\n\t"point_of_origin": "VANSCOY SOK 1VO SK CA",\n\t"customer_order_number": "80001877",\n\t"order_number": "1856",\n\t"bill_of_lading": "600929",\n\t"carrier_name": "CARON TRANSPORT LTD",\n\t"required_ship_date": "Nov 06,2023",\n\t"shipped_date": "Nov 06,2023",\n\t"transportation_mode": "Truck",\n\t"vehicle_number": "T/C NO.",\n\t"routing_info": "UNIVAR CANADA LTD.",\n\t"invoice_to_buyer": "Chemtrade West Limited Partnership",\n\t"consignee_number": "600929",\n\t"net_weight": "21.000 Tonne",\n\t"ticket_number": "N/A",\n\t"outbound_date": "N/A"\n}\n```\n```'`
I have started to notice this error at high-frequency after OpenAI dev day. Any idea what I might be doing wrong?
### Expected behavior
The output should only have one triple-backticks at the end and the output parser should parse the output properly.
|
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| 1,984,613,480 |
PR_kwDOIPDwls5e-yoX
| 13,100 |
Get project from langchain sdk
|
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[] | null | 1 | 2023-11-09T00:31:18 | 2023-11-09T01:10:13 | 2023-11-09T01:10:12 |
COLLABORATOR
| null |
## Description
We need to centralize the API we use to get the project name for our tracers. This PR makes it so we always get this from a shared function in the langsmith sdk.
## Dependencies
Upgraded langsmith from 0.52 to 0.62 to include the new API `get_tracer_project`
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PR_kwDOIPDwls5e-w50
| 13,099 |
Fix langchain.llms OpenAI completion doesn't work due to v1 client update
|
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| false | null |
[] | null | 10 | 2023-11-09T00:21:19 | 2023-11-10T19:14:25 | 2023-11-09T23:12:19 |
CONTRIBUTOR
| null |
This commit fixes the issue that langchain.llms OpenAI completion stopped working since the V1 openai client update.
Replace this entire comment with:
- **Description:** This PR fixes the issue [AttributeError: module 'openai' has no attribute 'Completion'](https://github.com/langchain-ai/langchain/issues/12967) similar to https://github.com/langchain-ai/langchain/commit/8e0cb2eb84539b249198f98f7647a1f24b2392c2 and https://github.com/langchain-ai/langchain/pull/12969,
- **Issue:** https://github.com/langchain-ai/langchain/issues/12967,
- **Dependencies:** `openai` v1.x.x client,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:** @dosuken123
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
|
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I_kwDOIPDwls52Sj84
| 13,098 |
Serialize failing - Can't use with_fallbacks with MapReduceChain/Summarization: Can't instantiate abstract class BaseLanguageModel with abstract methods agenerate_prompt...
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[] | null | 3 | 2023-11-08T23:48:38 | 2023-11-09T00:02:59 | null |
CONTRIBUTOR
| null |
I see the following when using AzureChatOpenAI with with_fallbacks. Removing with_fallbacks doesn't cause this issue.
`Can't instantiate abstract class BaseLanguageModel with abstract methods agenerate_prompt, apredict, apredict_messages, generate_prompt, invoke, predict, predict_messages (type=type_error)`
### System Info
Pydantic v1, same with v2
**Lanchain 0.0.295**
### Who can help?
@hwchase17
@agola11
@ey
### 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
This doesn't work:
``` fallback_chat_model = ChatOpenAI(model_name="model_name")
primary_chat_model = AzureChatOpenAI()
chat_model = primary_chat_model.with_fallbacks([fallback_chat_model])
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=primary_chat_model.get_num_tokens,
)
```
### Expected behavior
Should work as this, but with fallback support
``` fallback_chat_model = ChatOpenAI(model_name="model_name")
primary_chat_model = AzureChatOpenAI()
chat_model = primary_chat_model #.with_fallbacks([fallback_chat_model])
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=primary_chat_model.get_num_tokens,
)
```
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PR_kwDOIPDwls5e-mxx
| 13,097 |
Make jinachat_api_key a SecretStr
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[] | null | 2 | 2023-11-08T23:24:22 | 2023-12-05T04:50:11 | 2023-12-05T04:50:10 |
CONTRIBUTOR
| null |
Description: This pull request removes secrets present in raw format
Issue: Jinachat api key was exposed when printing out the langchain object https://github.com/langchain-ai/langchain/issues/12165
Maintainer: @eyurtsev
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I_kwDOIPDwls52SVau
| 13,096 |
make text fails
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[] | null | 3 | 2023-11-08T22:39:37 | 2023-11-09T00:50:57 | null |
NONE
| null |
### System Info
make text
Running Sphinx v4.5.0
loading pickled environment... done
[autosummary] generating autosummary for: index.rst
building [mo]: targets for 0 po files that are out of date
building [text]: targets for 0 source files that are out of date
updating environment: 0 added, 1 changed, 0 removed
reading sources... [100%] index
....
Exception occurred:
File "/lib/python3.10/site-packages/sphinx/registry.py", line 354, in create_translator
setattr(translator, 'visit_' + name, MethodType(visit, translator))
TypeError: first argument must be callable
Full path of file registry.py is truncated.
### 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
Run make text in docs/api_reference folder
### Expected behavior
Documentation output in .rst format
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I_kwDOIPDwls52SRJf
| 13,095 |
'RunnableWithFallbacks' object has no attribute 'get_num_tokens'
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[] | null | 7 | 2023-11-08T22:28:33 | 2023-11-08T23:45:31 | null |
CONTRIBUTOR
| null |
### System Info
llm/chat_models created using with_fallback() throw attribute error when trying to access their get_num_tokens() function. This could potentially break several use cases where one does chat_model.get_num_tokens(). I believe even the docs access it as such (For example in the [MapReduce using LCEL](https://python.langchain.com/docs/modules/chains/document/map_reduce#recreating-with-lcel) docs)
My use case was as such:
```
fallback_chat_model = ChatOpenAI(model_name="model_name", temperature=0)
primary_chat_model = AzureChatOpenAI(temperature=0)
chat_model = primary_chat_model.with_fallbacks([fallback_chat_model])
## Split text using length_function and RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=chat_model.get_num_tokens,
)
texts = text_splitter.create_documents([text_sentence_numbered])
```
Of course, I can just use primary_chat_model's get_num_tokens if it's equivalent to the fallback's but in the case it's not, this would be a bigger issue. Worse still, this may be used within chains like MapReduceChain etc and many haven't even begun the switch to LCEL.
**Langchain Version 0.0.295**
### Who can help?
@eyurtsev @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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
fallback_chat_model = ChatOpenAI(model_name="model_name", temperature=0)
primary_chat_model = AzureChatOpenAI(temperature=0)
chat_model = primary_chat_model.with_fallbacks([fallback_chat_model])
## Split text using length_function and RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
length_function=chat_model.get_num_tokens,
)
texts = text_splitter.create_documents([text_sentence_numbered])
```
### Expected behavior
get_num_tokens() is accessible and based on the fallback/original chat_model or llm being used.
|
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PR_kwDOIPDwls5e-SNO
| 13,094 |
Add RunnableSequence documentation
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COLLABORATOR
| null |
Add RunnableSequence documentation
|
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PR_kwDOIPDwls5e-SIk
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update more azure docs
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PR_kwDOIPDwls5e-Ra_
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Make javelin_ai_gateway_api_key a SecretStr
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CONTRIBUTOR
| null |
**Description:** Add masking of API Key for javelin_ai_gateway,
**Issue:** https://github.com/langchain-ai/langchain/issues/12165,
**Dependencies:** None,
**Tag maintainer:** @eyurtsev,
**Twitter handle:** @hiroshitashir
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PR_kwDOIPDwls5e-MFD
| 13,091 |
update azure embedding docs
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls52R9zR
| 13,090 |
Send back annotations sent by OpenAI for content filtered requests (and responses)
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NONE
| null |
### Feature request
Is it possible to send back the annotations in the response when a request / response is content filtered by AzureOpenAI? Some prompts are content filtered by Azure and sometimes the response to certain prompts are content filtered as well.
Azure OpenAI sends back the annotations https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter?tabs=python#annotations-preview
Langchain needs to capture these annotations and send it along with the response
### Motivation
This feature will help us to respond to our uses why a particular prompt was content filtered. Today we can only tell our users whether or not a prompt was content filtered by Azure but when the users ask why the filtration was applied, we have no definite answer. With these annotations we can tell our users why the content filtration was applied
### Your contribution
No, not at the moment
|
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PR_kwDOIPDwls5e-Hu0
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bump 332
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Document RunnableWithFallbacks
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COLLABORATOR
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Add documentation to RunnableWithFallbacks
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Issue: Chroma tests are buggy
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[] | null | 1 | 2023-11-08T20:56:30 | 2023-11-08T20:56:58 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
The tests for Langchain's `Chroma` are currently broken. The reason is:
- By default, chroma persists locally, for ease of use
- All the tests use the same collection name
- Thus, when a new `Chroma` is created, it sees the collections persisted from previous tests.
This causes inconsistent behavior, since the contents of the collection depend on the order of the tests, not just what the test itself added.
### Suggestion:
The solution is to create fixtures which appropriately teardown the `Chroma` after every test.
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PR_kwDOIPDwls5e9_88
| 13,086 |
bump 331rc3 exp 39
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[] | null | 1 | 2023-11-08T20:52:45 | 2023-11-08T21:00:14 | 2023-11-08T21:00:13 |
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PR_kwDOIPDwls5e9_Jr
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Don't pass EF to chroma
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[] | null | 5 | 2023-11-08T20:49:53 | 2023-11-21T05:33:13 | 2023-11-08T20:55:35 |
CONTRIBUTOR
| null |
- **Description:**
Recently Chroma rolled out a breaking change on the way we handle embedding functions, in order to support multi-modal collections.
This broke the way LangChain's `Chroma` objects get created, because we were passing the EF down into the Chroma collection: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023
However, internally, we are never actually using embeddings on the chroma collection - LangChain's `Chroma` object calls it instead. Thus we just don't pass an `embedding_function` to Chroma itself, which fixes the issue.
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PR_kwDOIPDwls5e9-NR
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Add Wrapping Library Metadata to MongoDB vector store
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[] | null | 3 | 2023-11-08T20:46:33 | 2023-11-17T06:20:05 | 2023-11-17T06:20:04 |
CONTRIBUTOR
| null |
**Description**
MongoDB drivers are used in various flavors and languages. Making sure we exercise our due diligence in identifying the "origin" of the library calls makes it best to understand how our Atlas servers get accessed.
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Add more runnable documentation
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COLLABORATOR
| null |
- Adding documentation to the runnable.
- Documentation is not organized in the best way for the runnable; i.e., in
terms of LCEL vs. other standard methods, will follow up with more edits.
|
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OpenAIEmbeddings
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[] | null | 3 | 2023-11-08T19:37:37 | 2023-11-09T22:22:17 | null |
NONE
| null |
### System Info
Traceback:
File "/home/appuser/venv/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 534, in _run_script
exec(code, module.__dict__)
File "/app/demo-chatbot/updatedapp.py", line 351, in <module>
asyncio.run(main())
File "/usr/local/lib/python3.9/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/usr/local/lib/python3.9/asyncio/base_events.py", line 647, in run_until_complete
return future.result()
File "/app/demo-chatbot/updatedapp.py", line 214, in main
vectors = file.get_vector()
File "/app/demo-chatbot/file.py", line 39, in get_vector
self.save_vector()
File "/app/demo-chatbot/file.py", line 30, in save_vector
embeddings = OpenAIEmbeddings(openai_api_key=st.secrets["OPEN_AI_KEY"])
File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic/main.py", line 1102, in pydantic.main.validate_model
File "/home/appuser/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 166, in validate_environment
values["client"] = openai.Embedding
### Who can help?
@agola11 OpenAI changed the code https://platform.openai.com/docs/guides/embeddings/what-are-embeddings
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just follow a funciton similar to
def save_vector(self):
self.create_images()
self.extract_text()
loader = TextLoader(f"{self.dir}/info.txt")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vectors = FAISS.from_documents(documents, embeddings)
### Expected behavior
No traceback adding .create
|
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I_kwDOIPDwls52RRWc
| 13,081 |
Tests are failing in local development
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[] | null | 4 | 2023-11-08T19:12:43 | 2023-11-08T19:36:34 | null |
CONTRIBUTOR
| null |
### System Info
langchain cloned
peortry with python v3.11.4
openai v1.1.1
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
cloned the repositpry
cd to `libs/langchain`
```
poetry install --with test
```
```
make test
```
output
```
....
FAILED tests/unit_tests/llms/test_anyscale.py::test_api_key_is_secret_string - AttributeError: module 'openai' has no attribute 'ChatCompletion'
FAILED tests/unit_tests/llms/test_anyscale.py::test_api_key_masked_when_passed_from_env - AttributeError: module 'openai' has no attribute 'ChatCompletion'
FAILED tests/unit_tests/llms/test_anyscale.py::test_api_key_masked_when_passed_via_constructor - AttributeError: module 'openai' has no attribute 'ChatCompletion'
FAILED tests/unit_tests/llms/test_gooseai.py::test_api_key_is_secret_string - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/llms/test_gooseai.py::test_api_key_masked_when_passed_via_constructor - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/llms/test_gooseai.py::test_api_key_masked_when_passed_from_env - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/llms/test_openai.py::test_openai_model_param - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/llms/test_openai.py::test_openai_model_kwargs - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/llms/test_openai.py::test_openai_incorrect_field - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/llms/test_openai.py::test_openai_retries - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/llms/test_openai.py::test_openai_async_retries - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_dump.py::test_serialize_openai_llm - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_dump.py::test_serialize_llmchain - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_dump.py::test_serialize_llmchain_env - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_dump.py::test_serialize_llmchain_with_non_serializable_arg - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_load.py::test_loads_openai_llm - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_load.py::test_loads_llmchain - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_load.py::test_loads_llmchain_env - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_load.py::test_loads_llmchain_with_non_serializable_arg - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_load.py::test_load_openai_llm - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_load.py::test_load_llmchain - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_load.py::test_load_llmchain_env - AttributeError: module 'openai' has no attribute 'Completion'
FAILED tests/unit_tests/load/test_load.py::test_load_llmchain_with_non_serializable_arg - AttributeError: module 'openai' has no attribute 'Completion'
==================================================================================== 23 failed, 1348 passed, 270 skipped, 24 warnings in 16.11s =========================================================
```
### Expected behavior
All tests should pass or skipped without failing.
|
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PR_kwDOIPDwls5e9YYo
| 13,079 |
Improve CSV reader which can't call .strip() on NoneType
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[] | null | 1 | 2023-11-08T18:43:42 | 2023-11-13T07:51:40 | 2023-11-13T07:51:40 |
CONTRIBUTOR
| null |
Improve CSV reader which can't call .strip() on NoneType if there are less cells in the row compared to the header
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:**
I have a CSV file as followed
```
headerA,headerB,headerC
v1A,v1B,v1C,
v2A,v2B
v3A,v3B,v3C
```
In this case, row 2 is missing a value, which results in reading a None type. The strip() method can not be called on None, hence raising. In this PR I am making the change to only call strip if the value if not None.
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5e9WjE
| 13,078 |
added `Cookbooks` link
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[] | null | 2 | 2023-11-08T18:37:41 | 2023-11-14T19:01:05 | 2023-11-14T18:52:48 |
COLLABORATOR
| null |
It is a temporary solution before major documents refactoring.
Related to #13070 (not solving it)
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PR_kwDOIPDwls5e9Ura
| 13,077 |
update root listeners
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COLLABORATOR
| null |
a nfcampos special
|
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| 1,984,144,941 |
I_kwDOIPDwls52Q6Yt
| 13,076 |
how to use reranker model with langchain in retrievalQA case?
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[] | null | 10 | 2023-11-08T18:06:30 | 2023-12-18T04:32:20 | null |
NONE
| null |
### System Info
version 0.0.331
### 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
omit
### Expected behavior
I would like to use BAAI/bge-reranker-large model as the reranker to rerank the intial retrieval results,
how to use this model with langchain ?
I have seen the demo with Cohere ranker,
but how to use ohter reranker models?
Does langchain support other reranker models except for CohereReranker?
|
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I_kwDOIPDwls52Q57s
| 13,075 |
Issue: AgentExecutor typings should accept Runnable for the agent property (to support LCEL agent)
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[] | null | 1 | 2023-11-08T18:05:14 | 2023-11-08T18:08:03 | null |
NONE
| null |
### Issue you'd like to raise.
Using LCEL as suggested on the [docs](https://python.langchain.com/docs/modules/agents/), combined with `AgentExecutor`, generates a typing error when passing the runnable agent to the `AgentExecutor` constructor. This is because `AgentExecutor` defines its `agent` property as of type `BaseSingleActionAgent | BaseMultiActionAgent`:
https://github.com/langchain-ai/langchain/blob/55aeff6777431dc24e48f018e39aa418f95a6489/libs/langchain/langchain/agents/agent.py#L728-L731
So:
```python
agent = {
...
} | prompt | llm | OpenAIFunctionsAgentOutputParser()
# agent would be of type Runnable[Unknown, Unknown]
# but the typing on AgentExecutor only takes a BaseSingleActionAgent
# or a BaseMultiActionAgent as a valid agent
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
# ↳ "Runnable[Unknown, Unknown]" is incompatible with "BaseSingleActionAgent"
# ↳ "Runnable[Unknown, Unknown]" is incompatible with "BaseMultiActionAgent"
```
### Suggestion:
`AgentExecutor` should accept a `Runnable` for its agent property
|
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PR_kwDOIPDwls5e9EzY
| 13,074 |
Add RunnableRetry Documentation
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[] | null | 1 | 2023-11-08T17:41:31 | 2023-11-08T18:20:20 | 2023-11-08T18:20:19 |
COLLABORATOR
| null |
Add retry documentation
|
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I_kwDOIPDwls52QscA
| 13,073 |
[Feature Request] AnthropicFunctionsAgent
|
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open
| false | null |
[] | null | 1 | 2023-11-08T17:29:21 | 2023-11-08T17:29:59 | null |
NONE
| null |
### Feature request
Hi, I'd like to request a feature for a `AnthropicFunctionsAgent` built on top of `AnthropicFunctions` and ideally compatible with `create_conversational_retrieval_agent`.
### Motivation
Everyone working with Anthropic models could use an Agents class!
### Your contribution
Can't currently.
|
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| 1,984,077,044 |
I_kwDOIPDwls52Qpz0
| 13,072 |
Running Ollama asynchronously generates a warning: RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited (langchain/llms/ollama.py:164)
|
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[] | null | 5 | 2023-11-08T17:23:48 | 2023-12-26T09:28:48 | null |
NONE
| null |
### System Info
python==3.11
langchain==0.0.326
ollama==v0.1.8
### Who can help?
@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
- [X] Chains
- [X] Callbacks/Tracing
- [X] Async
### Reproduction
Steps to reproduce the behavior:
```
llm = Ollama(model='llama2')
r = await llm.agenerate(['write a limerick about babies'])
print('\n'.join([t[0].text for t in r.generations]))
```
Generates the following output:
```
[/path/to/python3.11/site-packages/langchain/llms/ollama.py:164](https://file+.vscode-resource.vscode-cdn.net/Users/gburns/miniconda3/envs/alhazen/lib/python3.11/site-packages/langchain/llms/ollama.py:164): RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited
run_manager.on_llm_new_token(
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
Sure! Here is a limerick about babies:
everybody loves babies, they're so sweet;
their coos and snuggles can't be beat.
They suck their thumbs and play with toes,
and bring joy to all who sees.
```
### Expected behavior
The generation should occur without the warning.
The error is due to the `_OllamaCommon._stream_with_aggregation(` function not being able to distinguish between being called in a blocking or an async context.
The reason this is important is that sometimes Ollama gets stuck in a generation (taking a long time to complete) and I would like to be able to call a timeout on the underlying process. The following code can do this, but we get the warning as previously described (note, this requires that ollama be running in the background).
```
def _callback(fut: asyncio.Future):
if fut.cancelled() or not fut.done():
print("Timed out! - Terminating server")
fut.cancel()
async def run_llm(llm, prompt, timeout=300):
# create task
task = asyncio.create_task(llm.agenerate([prompt]))
task.add_done_callback(_callback)
# try to await the task
try:
r = await asyncio.wait_for(task, timeout=timeout)
except asyncio.TimeoutError as ex:
print(ex)
if r is not None:
return '\n'.join([t[0].text for t in r.generations])
else:
return ''
text = await llm.agenerate(['write a limerick about babies'])
print(text)
```
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| 1,984,016,062 |
I_kwDOIPDwls52Qa6-
| 13,070 |
DOC: no `cookbooks` search
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[] | null | 1 | 2023-11-08T16:48:44 | 2023-11-08T18:06:13 | null |
COLLABORATOR
| null |
### Issue with current documentation:
Related to the #13036.
I've searched in the LangChain documentation by "Search" button and didn't find existing example which was in `cookbooks`.
### Idea or request for content:
Is it possible to add `cookbooks` into the Documentation search? Or maybe right into the Main menu?
|
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| 1,983,971,005 |
I_kwDOIPDwls52QP69
| 13,069 |
PyPI versions issue e.g. langchain==0.0.331 is not newer than langchain==0.0.79
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[] | null | 3 | 2023-11-08T16:23:02 | 2023-11-16T12:28:10 | 2023-11-16T12:28:10 |
NONE
| null |
### System Info
Unix and Windows
### 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
```
pip install langchain==0.0.79
pip install langchain>=0.0.331
```
### Expected behavior
I would expect to install the latest version 0.0.331 but its not. Seems pip undestands that 0.0.79 is a higher version than 0.0.331?
|
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| 1,983,943,414 |
PR_kwDOIPDwls5e8gnN
| 13,068 |
oai assistant multiple actions
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[] | null | 1 | 2023-11-08T16:07:51 | 2023-11-08T16:25:38 | 2023-11-08T16:25:38 |
COLLABORATOR
| null | null |
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| 1,983,852,453 |
I_kwDOIPDwls52Py-l
| 13,065 |
Issue: STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION sometimes not return the "Final Answer"
|
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[] | null | 1 | 2023-11-08T15:22:08 | 2023-11-10T02:04:28 | null |
NONE
| null |
### Issue you'd like to raise.
The agent(STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION) sometimes does not respond the "Final Answer" instead of the AI's thought.
### Actual result:
The human is asking for the default xxxxxx. The tool has provided the information. I will now relay this information to the human.
### Expected result:
{
"action": "Final Answer",
"action_input": "The information comes from the tool."
}
### System info:
AzureChatOpenAI / gpt-4
This is a sometimes issue. I have tried with the smith playground, it can happen as well. Any thoughts about it?
|
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reopened
| null | null |
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| 1,983,852,320 |
I_kwDOIPDwls52Py8g
| 13,064 |
Seeing an error ModuleNotFoundError: No module named 'langchain.globals'
|
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[] | null | 1 | 2023-11-08T15:22:05 | 2023-11-08T15:40:42 | null |
NONE
| null |
### System Info
LangChain version pinned to "^0.0.278" (using Poetry)
Python 3.11.5
Other modules from langchain (such as langchain.cache and langchain.chains) are imported within the same file in the application code and are able to be found. Only the `langchain.globals` module is not being recognized
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Import langchain.globals
Error happens on application startup
### Expected behavior
Expected to be able to find the module, as langchain's other module's are able to be found by the same application code
|
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PR_kwDOIPDwls5e78ao
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Add Tavily to tools init file
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CONTRIBUTOR
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Add Tavily to tools _init_ file
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gpt researcher
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I_kwDOIPDwls52PPCc
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ChromaDb EmbeddingFunction definition updated
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[] | null | 8 | 2023-11-08T14:11:41 | 2023-12-10T12:44:35 | 2023-11-08T21:39:34 |
NONE
| null |
### System Info
Using Google Colab Free version with T4 GPU.
chromadb==0.4.16
### Who can help?
@agola11 @hwchase17
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
As per the latest Chromadb migration logs ([link](https://docs.trychroma.com/migration#migration-to-0416---november-7-2023)) `EmbeddingFunction` defnition has been updated and it affects all the custom made embedding function.
What this means is the `langchain.embeddings.HuggingFaceBgeEmbeddings` is inconsistent with this new definition and throws the following error:
```py
ValueError: Expected EmbeddingFunction.__call__ to have the following signature: odict_keys(['self', 'input']), got odict_keys(['self', 'args', 'kwargs'])
Please see https://docs.trychroma.com/embeddings for details of the EmbeddingFunction interface.
Please note the recent change to the EmbeddingFunction interface: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023
```
The above code can be reproduced by inserting documents into Chromadb embedded using `HuggingFaceBgeEmbeddings` like so:
```py
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceBgeEmbeddings
from transformers import AutoTokenizer
embedding_function = HuggingFaceBgeEmbeddings(
model_name="BAAI/bge-base-en-v1.5",
model_kwargs={'device': 'cuda'},
encode_kwargs={'normalize_embeddings': True},
query_instruction="Represent this sentence for searching relevant passages: "
)
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-base-en-v1.5')
text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
tokenizer, chunk_size=100, chunk_overlap=0
)
text = 'Some text that needs to be embedded.'
print(len(embedding_function.embed_query(text))) # works so far
splits = text_splitter.create_documents([text])
db = Chroma.from_documents(splits, embedding_function, persist_directory="./chroma_db")
```
I am not sure, but the answer might lie in correcting the `HuggingFaceBgeEmbeddings` class : [link](https://github.com/langchain-ai/langchain/blob/1f27104626fc71a5199df965011810426dd2eede/libs/langchain/langchain/embeddings/huggingface.py#L188) ?
### Expected behavior
The expected behaviour would have made a valid `db` object upon running the code
```py
db = Chroma.from_documents(splits, embedding_function, persist_directory="./chroma_db")
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I_kwDOIPDwls52O-pU
| 13,060 |
Issue: UnstructuredPDFLoader doesn't support Unstructured functionalities
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NONE
| null |
### Issue you'd like to raise.
When initializing the langchain UntructuredPDFLoader e.g. as follows
` loader = UnstructuredPDFLoader(downloaded_file, mode='elements')`
This method calls the following function (see langchain/document_loaders/pdf.py):
` class UnstructuredPDFLoader(UnstructuredFileLoader): `
`def _get_elements(self) -> List: `
from unstructured.partition.pdf import partition_pdf
return partition_pdf(filename=self.file_path, **self.unstructured_kwargs)
The function `partition_pdf()` from Unstructured allows one to decide between passing either a file_path to a file in storage, or alternatively a ByteStream pointing to a file in memory but it does not allow one to pass both. Langchain forces users to pass the parameter ` file_path `and thus one cannot use the option of using a stream to load a file (as Unstructured doesn't expect both a file_path and a stream).
### Suggestion:
Remove the part which forces one to pass a ` file_path ` to UnstructuredPDFLoader initializiation. With this change, users can decide to pass a Stream in the `unstructured_kwargs ` field and thus use the loader.
To test this I rewrote the _get_elements function as follows and like this it works to pass a stream:
`
def _get_elements(self) -> List: `
` from unstructured.partition.pdf import partition_pdf
return partition_pdf(**self.unstructured_kwargs) `
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I_kwDOIPDwls52Ouv9
| 13,059 |
Langchain agent which only returns tools response without observations.
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NONE
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I have 4 tools which return responses of apis inside function. Now I want to build a system that only returns the response of api without any observations. Also agents should have memories.
Is it possible with a langchain agent. If yes can you tell me how ???
|
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| 1,983,512,624 |
I_kwDOIPDwls52OgAw
| 13,058 |
Issue with user_agent error when creating a CohereReRank object in LangChain
|
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[] | null | 3 | 2023-11-08T12:38:34 | 2023-11-25T08:19:46 | null |
NONE
| null |
### System Info
last version of langchain and cohere
### Who can help?
@@agola11
I am encountering an error related to the user_agent when attempting to create a CohererRank object in the LangChain p. I have verified that I am passing a valid cohere_api_key and can successfully use the rerank function from Cohere directly. However, when trying to utilize it through the LangChain project, I encounter this specific issue related to the user_agent.
[/usr/local/lib/python3.10/dist-packages/langchain/retrievers/document_compressors/cohere_rerank.py](https://localhost:8080/#) in validate_environment(cls, values)
53 values, "cohere_api_key", "COHERE_API_KEY"
54 )
---> 55 client_name = values["user_agent"]
56 values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
57 return values
KeyError: 'user_agent'
### 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
import os
import getpass
os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:")
from langchain.llms import OpenAI
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
compressor = CohereRerank()
### Expected behavior
i want to create an object of CohereReRank.
|
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| 1,983,455,592 |
PR_kwDOIPDwls5e61dH
| 13,057 |
Refine Weaviate docs and add RAG example
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[] | null | 1 | 2023-11-08T12:09:58 | 2023-11-14T07:57:04 | 2023-11-13T18:59:19 |
CONTRIBUTOR
| null |
- **Description:** Refine Weaviate tutorial and add an example for Retrieval-Augmented Generation (RAG)
- **Issue:** (not applicable),
- **Dependencies:** none
- **Tag maintainer:** @baskaryan <!--
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Twitter handle:** @helloiamleonie
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| 1,983,440,119 |
I_kwDOIPDwls52OOT3
| 13,056 |
Updated to latest langchain version but still getting OpenAI embeddings error
|
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[] | null | 5 | 2023-11-08T12:01:41 | 2023-11-13T09:37:43 | null |
NONE
| null |
### System Info
LangChain version = 0.0.331
Openai version = 1.1.1
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```Error creating LLM: module 'openai' has no attribute 'Embedding'
aai_app | Error creating LLM: module 'openai' has no attribute 'Embedding'
aai_app | Traceback (most recent call last):
aai_app | File "/code/aai/apps/slack_bot/views.py", line 160, in call_open_ai
aai_app | reply = open_ai.execute_query(question)
aai_app | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
aai_app | File "/code/aai/apps/affiliateai/services/open_ai/open_ai.py", line 130, in execute_query
aai_app | named_entity_recognition = NamedEntityRecognition(
aai_app | ^^^^^^^^^^^^^^^^^^^^^^^
aai_app | File "/code/aai/apps/affiliateai/services/open_ai/named_entity_recognition.py", line 41, in __init__
aai_app | self.llm_embeddings = self.llm.create_llm(
aai_app | ^^^^^^^^^^^^^^^^^^^^
aai_app | File "/code/aai/apps/affiliateai/services/open_ai/llm_manager.py", line 63, in create_llm
aai_app | return OpenAIEmbeddings(
aai_app | ^^^^^^^^^^^^^^^^^
aai_app | File "/usr/local/lib/python3.11/site-packages/pydantic/v1/main.py", line 339, in __init__
aai_app | values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
aai_app | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
aai_app | File "/usr/local/lib/python3.11/site-packages/pydantic/v1/main.py", line 1050, in validate_model
aai_app | input_data = validator(cls_, input_data)
aai_app | ^^^^^^^^^^^^^^^^^^^^^^^^^^^
aai_app | File "/usr/local/lib/python3.11/site-packages/langchain/embeddings/openai.py", line 284, in validate_environment
aai_app | values["client"] = openai.Embedding
aai_app | ^^^^^^^^^^^^^^^^
aai_app | AttributeError: module 'openai' has no attribute 'Embedding'
aai_app |
aai_app | module 'openai' has no attribute 'Embedding'
aai_app | Traceback (most recent call last):
aai_app | File "/code/aai/apps/slack_bot/views.py", line 160, in call_open_ai
aai_app | reply = open_ai.execute_query(question)
aai_app | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
aai_app | File "/code/aai/apps/affiliateai/services/open_ai/open_ai.py", line 130, in execute_query
aai_app | named_entity_recognition = NamedEntityRecognition(
aai_app | ^^^^^^^^^^^^^^^^^^^^^^^
aai_app | File "/code/aai/apps/affiliateai/services/open_ai/named_entity_recognition.py", line 41, in __init__
aai_app | self.llm_embeddings = self.llm.create_llm(
aai_app | ^^^^^^^^^^^^^^^^^^^^
aai_app | File "/code/aai/apps/affiliateai/services/open_ai/llm_manager.py", line 63, in create_llm
aai_app | return OpenAIEmbeddings(
aai_app | ^^^^^^^^^^^^^^^^^
aai_app | File "/usr/local/lib/python3.11/site-packages/pydantic/v1/main.py", line 339, in __init__
aai_app | values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
aai_app | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
aai_app | File "/usr/local/lib/python3.11/site-packages/pydantic/v1/main.py", line 1050, in validate_model
aai_app | input_data = validator(cls_, input_data)
aai_app | ^^^^^^^^^^^^^^^^^^^^^^^^^^^
aai_app | File "/usr/local/lib/python3.11/site-packages/langchain/embeddings/openai.py", line 284, in validate_environment
aai_app | values["client"] = openai.Embedding
aai_app | ^^^^^^^^^^^^^^^^
aai_app | AttributeError: module 'openai' has no attribute 'Embedding'```
### Expected behavior
Not to see this error.
|
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I_kwDOIPDwls52OOEK
| 13,055 |
ValueError: Missing some input keys: - passed data requires input keys if containing { }
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[] | null | 3 | 2023-11-08T12:01:05 | 2023-11-08T18:52:53 | 2023-11-08T18:52:53 |
NONE
| null |
### System Info
Python 3.11.5
Langchain (pip show) 0.0.327
Windows OS
Visual Studio Code
### Who can help?
@eyurtsev
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I searched and was surprised this has not come up.
I am using LangChain for a RAG workflow - and when I send a document, if that document contains { } - it throws a missing key error - it is treating the content of the document, as it would a normal prompt where you might have "question {question}" and expect an input key of 'question' it then reports back that all of the { } are in fact different missing keys.
For example, my data contains this:
`"...1 2 ------------------------------------ {w14 w15 w16se w16cid w16 w16cex w16sdtdh wp14}{DP}{AD}{S::}"`
It will say that we are missing numerous keys:
`ValueError: Missing some input keys: {'AD', 'w14 w15 w16se w16cid w16 w16cex w16sdtdh wp14', ...}`
Now, I can clean the data prior to sending, but I was wondering whether it should behave like this given that this document is already within { } as content?
I use the "FewShotPromptTemplate" to create a prompt which includes a "Suffix" and my suffix is:
```
def get_suffix():
return """
Document: {content}
Question: {question}
"""
```
Here content is the content of the document that contains the { } set out above.
I build the prompt like this:
```
prompt_template = FewShotPromptTemplate(
examples = examples,
example_prompt = get_prompt_template(example_template, example_variables),
prefix = prefix,
suffix = suffix,
input_variables = input_variables
)
prompt = prompt_template.format(question=question, context=context)
return prompt
```
I also did a test using another piece of code:
```
document_context = text_response + "{AD}"
prompt = ChatPromptTemplate.from_template("my_specific_prompt": {document}.\n{format_instructions}")
formated_prompt = prompt.format(**{"document": document_context, "format_instructions":output_parser.get_format_instructions()})
```
Introducing a random {AD} into the text response. It did not fail. It messed up the results, but it didn't actually cause any missing input key errors.
So this may be limited to the FewShotPromptTemplate?
### Expected behavior
I would have thought that anything passed within a curly bracket set would be considered as plain text, not parsed for further keys that might be embedded in that curly bracket set and throw an error when it cannot find them?
Maybe I am wrong, but that is what I would have expected and is what appears to happen when using the ChatPromptTemplate.from_template?
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I_kwDOIPDwls52NS0W
| 13,054 |
The prompt word format misleads the output content of the large model
|
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open
| false | null |
[] | null | 3 | 2023-11-08T09:54:19 | 2023-11-09T01:10:16 | null |
NONE
| null |
### System Info
langchain 0.0.331
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
response_schemas = [
ResponseSchema(type='list', name='disease', description='disease name')
]
**prompt**
The output should be a markdown code snippet formatted in the following schema, including the leading and trailing "```json" and "```":
```json
{
"disease": list // disease name
}
```
**output**
{
"disease": "感冒" // disease name
}
This output cannot be parsed by StructuredOutputParser，Because it contains notes。
### Expected behavior
Expect to generate prompt words：
**prompt**
The output should be a markdown code snippet formatted in the following schema, including the leading and trailing "```json" and "```"，and the field "disease" meas "disease name".
```json
{
"disease": list
}
```
Expected output：
{
"disease": "感冒"
}
|
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| 1,983,141,722 |
I_kwDOIPDwls52NFda
| 13,053 |
OpenAI instance of Runnable expected
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[] | null | 5 | 2023-11-08T09:27:14 | 2024-01-11T22:04:13 | 2023-11-22T06:55:19 |
NONE
| null |
### System Info
# Hi there,
I have started learning about Langchain today. I was creating my first langchain prompt template but something doesn't seem to work.
Here is my code in main.py:
```python
from openai import OpenAI
from dotenv import load_dotenv
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
import os
# Load the .env file
load_dotenv()
# Create an instance of the OpenAI class
def generate_pet_name(animal_type="dog"):
prompt_template = PromptTemplate(
input_variables=['animal_type'],
template="I have a {animal_type} as my pet. Suggest me a name for it.",
)
name_chain = LLMChain(
llm=OpenAI(),
prompt=prompt_template,
)
response = name_chain({'animal_type': animal_type})
print(response)
if __name__ == "__main__":
generate_pet_name(animal_type="dog")
```
While I think the code is okay and I have followed the GitHub get started it doesn't seem to work and throwing me this error:
```console
Traceback (most recent call last):
File "/Users/ss/Workspace/Ai/AMP/main.py", line 28, in <module>
generate_pet_name(animal_type="dog")
File "/Users/ss/Workspace/Ai/AMP/main.py", line 18, in generate_pet_name
name_chain = LLMChain(
^^^^^^^^^
File "/Users/ss/Workspace/Ai/AMP/.venv/lib/python3.11/site-packages/langchain/load/serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "/Users/ss/Workspace/Ai/AMP/.venv/lib/python3.11/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 2 validation errors for LLMChain
llm
instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)
llm
instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)
```
Please Help me as I am not that proficient in Python.
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just copy the code given there and run it. You will see the error.
### Expected behavior
Run the code and get a response without error
|
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| 1,983,011,615 |
PR_kwDOIPDwls5e5TPZ
| 13,052 |
Fixed a typo in smart_llm prompt
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[] | null | 1 | 2023-11-08T08:22:56 | 2023-12-06T03:16:19 | 2023-12-06T03:16:18 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls52MbYe
| 13,051 |
Bug after the openai updated in Embedding
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| false | null |
[] | null | 20 | 2023-11-08T07:56:36 | 2023-12-04T14:30:26 | 2023-11-08T21:39:20 |
NONE
| null |
### System Info
AWS Sagemaker DataScience3.0 Image.
### Who can help?
_No response_
### 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
here is my code, it worked before Nov 7th.
`Chroma.from_documents(documents=document, embedding=embeddings,)`
Then i get this error:
`ValueError: Expected EmbeddingFunction.__call__ to have the following signature: odict_keys(['self', 'input']), got odict_keys(['self', 'args', 'kwargs'])
Please see https://docs.trychroma.com/embeddings for details of the EmbeddingFunction interface.
Please note the recent change to the EmbeddingFunction interface: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023 `
### Expected behavior
Is anyone know how to fix this?
|
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I_kwDOIPDwls52MQ9r
| 13,050 |
Multiple values for keyword argument 'embedding_function'
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[] | null | 2 | 2023-11-08T07:26:31 | 2023-11-08T07:34:54 | null |
NONE
| null |
### System Info
Running on google colab.
Everything was working up until today, which makes me think it's openAi update-related.
Versions:
Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (**0.0.331**)
Requirement already satisfied: chromadb in /usr/local/lib/python3.10/dist-packages (**0.4.16**)
Openai version pinned to 0.28.1 as @hwchase17 recommended prior -- which had fixed my embeddings issue.
### 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
```python3
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents=splits, embedding_function=embeddings, persist_directory='/content/wtf')
vectorstore.persist()
retriever = vectorstore.as_retriever()
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-5-d086aa9b6782>](https://localhost:8080/#) in <cell line: 7>()
5
6 embeddings = OpenAIEmbeddings()
----> 7 vectorstore = Chroma.from_documents(documents=splits, embedding_function=embeddings, persist_directory='/content/wtf')
8 vectorstore.persist()
9 retriever = vectorstore.as_retriever()
1 frames
[/usr/local/lib/python3.10/dist-packages/langchain/vectorstores/chroma.py](https://localhost:8080/#) in from_texts(cls, texts, embedding, metadatas, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs)
618 Chroma: Chroma vectorstore.
619 """
--> 620 chroma_collection = cls(
621 collection_name=collection_name,
622 embedding_function=embedding,
TypeError: langchain.vectorstores.chroma.Chroma() got multiple values for keyword argument 'embedding_function'
### Expected behavior
It should run without an error and correctly embed the document splits, outputting the data in the persist directory.
|
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I_kwDOIPDwls52MIM6
| 13,049 |
No module named 'langserve.packages' when creating langchain-cli apps
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[] | null | 12 | 2023-11-08T07:02:02 | 2023-11-14T18:24:49 | 2023-11-14T18:24:20 |
NONE
| null |
### System Info
Windows WSL 2 Ubuntu
Python 3.10.6
langchain-0.0.331 langchain-cli-0.0.15 langserve-0.0.24 langsmith-0.0.60
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
In a new virtual environment with Python 3.10.6, After running:
`pip install -U "langchain-cli[serve]"`
which installs "langchain-0.0.331 langchain-cli-0.0.15 langserve-0.0.24 langsmith-0.0.60" as shown below:
Successfully installed PyYAML-6.0.1 SQLAlchemy-2.0.23 aiohttp-3.8.6 aiosignal-1.3.1 annotated-types-0.6.0 anyio-3.7.1 async-timeout-4.0.3 attrs-23.1.0 certifi-2023.7.22 charset-normalizer-3.3.2 click-8.1.7 colorama-0.4.6 dataclasses-json-0.6.1 exceptiongroup-1.1.3 fastapi-0.104.1 frozenlist-1.4.0 gitdb-4.0.11 gitpython-3.1.40 greenlet-3.0.1 h11-0.14.0 httpcore-1.0.1 httpx-0.25.1 httpx-sse-0.3.1 idna-3.4 jsonpatch-1.33 jsonpointer-2.4 langchain-0.0.331 langchain-cli-0.0.15 langserve-0.0.24 langsmith-0.0.60 markdown-it-py-3.0.0 marshmallow-3.20.1 mdurl-0.1.2 multidict-6.0.4 mypy-extensions-1.0.0 numpy-1.26.1 packaging-23.2 pydantic-2.4.2 pydantic-core-2.10.1 pygments-2.16.1 requests-2.31.0 rich-13.6.0 shellingham-1.5.4 smmap-5.0.1 sniffio-1.3.0 sse-starlette-1.6.5 starlette-0.27.0 tenacity-8.2.3 tomli-2.0.1 typer-0.9.0 typing-extensions-4.8.0 typing-inspect-0.9.0 urllib3-2.0.7 uvicorn-0.23.2 yarl-1.9.2
I try to run `langchain app new my-app --package rag-conversation`, and get the following error
Traceback (most recent call last):
File "/home/mert/REPOSITORIES/GenAI/langserve-template/langserve-env/bin/langchain", line 5, in <module>
from langchain_cli.cli import app
File "/home/mert/REPOSITORIES/GenAI/langserve-template/langserve-env/lib/python3.10/site-packages/langchain_cli/cli.py", line 6, in <module>
from langchain_cli.namespaces import app as app_namespace
File "/home/mert/REPOSITORIES/GenAI/langserve-template/langserve-env/lib/python3.10/site-packages/langchain_cli/namespaces/app.py", line 12, in <module>
from langserve.packages import get_langserve_export
ModuleNotFoundError: No module named 'langserve.packages'
### Expected behavior
Pulling template into a local repository
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PR_kwDOIPDwls5e43jY
| 13,048 |
refactor: Qdrant async improvements
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CONTRIBUTOR
| null |
#### Description:
This PR intends to simplify the async implementation of Qdrant vectorstore using the `AsyncQdrantClient` interface introduced in [`[email protected]`](https://github.com/qdrant/qdrant-client/releases/tag/v1.6.1).
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I_kwDOIPDwls52MDXs
| 13,047 |
Issue: UnstructuredExcelLoader
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[] | null | 2 | 2023-11-08T06:49:27 | 2023-11-08T06:59:41 | null |
NONE
| null |
from langchain.document_loaders import UnstructuredExcelLoader
loader = UnstructuredExcelLoader("example_data/stanley-cups.xlsx", mode="elements")
docs = loader.load()
docs[0]
in the above it gives ouput as
Document(page_content='\n \n \n Team\n Location\n Stanley Cups\n \n \n Blues\n STL\n 1\n \n \n Flyers\n PHI\n 2\n \n \n Maple Leafs\n TOR\n 13\n \n \n', metadata={'source': 'example_data/stanley-cups.xlsx', 'filename': 'stanley-cups.xlsx', 'file_directory': 'example_data', 'filetype': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'page_number': 1, 'page_name': 'Stanley Cups', 'text_as_html': '<table border="1" class="dataframe">\n <tbody>\n <tr>\n <td>Team</td>\n <td>Location</td>\n <td>Stanley Cups</td>\n </tr>\n <tr>\n <td>Blues</td>\n <td>STL</td>\n <td>1</td>\n </tr>\n <tr>\n <td>Flyers</td>\n <td>PHI</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Maple Leafs</td>\n <td>TOR</td>\n <td>13</td>\n </tr>\n </tbody>\n</table>', 'category': 'Table'})
when i pass the above to CharacterTextSplitter, it gives error because it expects in the different format.
text_splitter = CharacterTextSplitter(chunk_overlap=0, chunk_size=1000)
texts = text_splitter.split_documents(docs[0])
### Suggestion:
_No response_
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| 1,982,867,010 |
I_kwDOIPDwls52MCZC
| 13,046 |
看了新的OpenAI开发者大会，langchain还有存在的必要吗
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[] | null | 5 | 2023-11-08T06:46:32 | 2023-11-08T17:19:34 | null |
NONE
| null |
### Issue you'd like to raise.
新的chatgpt可以直接帮助人类分析，规划，似乎已经脱离了原本的软件开发模式，从人设计，到了ai设计了，langchain是不是已经过时了？
### Suggestion:
_No response_
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I_kwDOIPDwls52L_51
| 13,045 |
help: github util `comment_on_issue` unable to parse `action input` from agent
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[] | null | 4 | 2023-11-08T06:38:13 | 2023-11-12T18:52:11 | 2023-11-12T18:46:59 |
NONE
| null |
### Issue you'd like to raise.
I am using github tools along with an agent following example given (https://python.langchain.com/docs/integrations/toolkits/github), but the comment_on_issue tool is not able to parse the Action Input given by agent (the format seems to be same as given in the prompts), there seems to be some issue with escap sequnces.
Action Input: 2\n\nStarting to review docstrings and add sphinx\n\n
Error: ValueError: invalid literal for int() with base 10: '2\n\nStarting to review docstrings and add sphinx\n\n'
I think there might be some issue decoding (extra \ added to newline character). can anyone pls help with this?
### Suggestion:
_No response_
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I_kwDOIPDwls52L1yL
| 13,044 |
My LLM keeps rephrasing the question and it doesnt return source documents
|
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NONE
| null |
@dosu-bot
Below is my code and everytime I ask it a question, it rephrases the question then answers it for me. Help me to remove the rephrasing part. I did set it to False yet it still does it.
Also, I would like to return the source of the documents but its showing me this error:
File "C:\Users\Asus\Documents\Vendolista\hacka.py", line 178, in
main()
File "C:\Users\Asus\Documents\Vendolista\hacka.py", line 172, in main
result = qa({"question": user_input})
File "C:\Users\Asus\Documents\Vendolista.venv\lib\site-packages\langchain\chains\base.py", line 294, in call
final_outputs: Dict[str, Any] = self.prep_outputs(
File "C:\Users\Asus\Documents\Vendolista.venv\lib\site-packages\langchain\chains\base.py", line 390, in prep_outputs
self.memory.save_context(inputs, outputs)
File "C:\Users\Asus\Documents\Vendolista.venv\lib\site-packages\langchain\memory\chat_memory.py", line 35, in save_context
input_str, output_str = self._get_input_output(inputs, outputs)
File "C:\Users\Asus\Documents\Vendolista.venv\lib\site-packages\langchain\memory\chat_memory.py", line 27, in _get_input_output
raise ValueError(f"One output key expected, got {outputs.keys()}")
ValueError: One output key expected, got dict_keys(['answer', 'source_documents'])
Below is my code
import os
import json
import pandas as pd
LLM
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
Prompt
from langchain.prompts.prompt import PromptTemplate
from langchain.prompts.chat import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
Embeddings
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
Chain
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.chains import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.document_loaders.csv_loader import CSVLoader, UnstructuredCSVLoader
from langchain.document_loaders import DirectoryLoader
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field
from dotenv import load_dotenv
import time
import pandas as pd
from langchain.callbacks import StreamingStdOutCallbackHandler
from langchain.text_splitter import RecursiveCharacterTextSplitter
load_dotenv()
file_path = "C:\Users\Asus\Documents\Vendolista\home_depot_data.csv"
path = "C:\Users\Asus\Documents\Vendolista\home depot"
csv_loader = CSVLoader(file_path=path, encoding='utf-8')
csv_loader = DirectoryLoader(path,
glob="**/*.csv",
show_progress=True,
use_multithreading=True,
silent_errors=True,
loader_cls = CSVLoader)
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
documents = csv_loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=200,
chunk_overlap=50,
)
chunks = text_splitter.split_documents(documents)
chunks = documents
embeddings = OpenAIEmbeddings()
persist_directory = "C:\Users\Asus\OneDrive\Documents\Vendolista"
knowledge_base = Chroma(embedding_function=embeddings, persist_directory=persist_directory)
# Split the chunks into smaller batches
batch_size = 5000
for i in range(0, len(chunks), batch_size):
batch = chunks[i:i+batch_size]
knowledge_base.add_documents(batch)
Save the vector store to disk
knowledge_base.persist()
Load the vector store from disk
knowledge_base = Chroma(chunks, persist_directory=persist_directory, embedding_function=embeddings)
class Product(BaseModel):
"""Product details schema."""
url:str = Field(description="Full URL link to the product webpage on Homedepot.")
title:str = Field(description="Title of the product.")
description:str = Field(description="Description of the prodcut.")
brand:str = Field(description="Manufacturing brand of the product.")
price:float = Field(description="Unit selling price of the product.")
parser = PydanticOutputParser(pydantic_object=Product)
question_template = """ Make sure you understand the question as its very important for the user.
You never know what situation they are in and you need to ensure that its understood very well but do not repeat
or rewrite the question
Input: {question}
"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(question_template)
Chain for question generation
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
Chat Prompt
system_template = """
You are a friendly, conversational retail shopping assistant named RAAFYA.
You will always and always and always only follow these set of rules and nothing else no matter what:
You will provide the user answers based on the csv file that you can only read from which is called "home_depot_data.csv"
You will never mention the name of the dataset that you have. Just say "my data" instead
Focus 100% to understand exactly what the customer is looking for and only give him whats available based on the data.
Do not get anything or say anything that is not related to the data that you have and never provide wrong information.
Use the following context including product name descriptions, and keywords to show the shopper whats available,
help find what they want, and answer their questions related to your job
Never ever consider or think or even mention that you do not have access to the internet because it is not your job
and it is not your task. I will repeat it again and again, your information is only and only coming from the dataset
that you have which is called "home_depot_data.csv" but you must not mention that to anyone for security purposes
Everyime you answer a question, write on a new line "is there anything else you would like me to help you with?"
If a customer asked for a product and it is not available then say "Sorry it is currently unavailable but you can
reach out to our staff and ask them about it at [[email protected]](mailto:[email protected])"
If the person asked for more details then provide him the details based on the output parser that you have:
URL:
Title:
Description:
Brand:
Price:
Context:
{context}
"""
system_message_prompt = SystemMessagePromptTemplate.from_template(system_template)
Human Prompt
human_template="""{format_instructions}
Question: {question}"""
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
Inject instructions into the prompt template.
human_message_prompt = HumanMessagePromptTemplate(
prompt=PromptTemplate(
template=human_template,
input_variables=["question"],
partial_variables={"format_instructions": parser.get_format_instructions()}
)
)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
Chain for Q&A
answer_chain = load_qa_chain(llm,
chain_type="stuff",
prompt=chat_prompt)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
Chain
qa = ConversationalRetrievalChain(
retriever = knowledge_base.as_retriever(),
question_generator = question_generator,
combine_docs_chain = answer_chain,
memory=memory,
rephrase_question=False,
return_source_documents=True
)
def main():
while True:
user_input = input("What would you like to shop for: ")
if user_input.lower() in ["exit"]:
break
if user_input != "":
with get_openai_callback() as cb:
result = qa({"question": user_input})
print()
# print(cb)
# print()
if name == "main":
main()
|
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I_kwDOIPDwls52LkH-
| 13,043 |
My llm keeps rephrasing question and it doesnt return source documents
|
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NONE
| null |
@dosu-bot
Below is my code and everytime I ask it a question, it rephrases the question then answers it for me. Help me to remove the rephrasing part. I did set it to False yet it still does it.
Also, I would like to return the source of the documents but its showing me this error:
File "C:\Users\Asus\Documents\Vendolista\hacka.py", line 178, in <module>
main()
File "C:\Users\Asus\Documents\Vendolista\hacka.py", line 172, in main
result = qa({"question": user_input})
File "C:\Users\Asus\Documents\Vendolista\.venv\lib\site-packages\langchain\chains\base.py", line 294, in __call__
final_outputs: Dict[str, Any] = self.prep_outputs(
File "C:\Users\Asus\Documents\Vendolista\.venv\lib\site-packages\langchain\chains\base.py", line 390, in prep_outputs
self.memory.save_context(inputs, outputs)
File "C:\Users\Asus\Documents\Vendolista\.venv\lib\site-packages\langchain\memory\chat_memory.py", line 35, in save_context
input_str, output_str = self._get_input_output(inputs, outputs)
File "C:\Users\Asus\Documents\Vendolista\.venv\lib\site-packages\langchain\memory\chat_memory.py", line 27, in _get_input_output
raise ValueError(f"One output key expected, got {outputs.keys()}")
ValueError: One output key expected, got dict_keys(['answer', 'source_documents'])
Below is my code
import os
import json
import pandas as pd
# LLM
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
# Prompt
from langchain.prompts.prompt import PromptTemplate
from langchain.prompts.chat import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
# Embeddings
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
# Chain
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.chains import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.document_loaders.csv_loader import CSVLoader, UnstructuredCSVLoader
from langchain.document_loaders import DirectoryLoader
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field
from dotenv import load_dotenv
import time
import pandas as pd
from langchain.callbacks import StreamingStdOutCallbackHandler
from langchain.text_splitter import RecursiveCharacterTextSplitter
load_dotenv()
file_path = "C:\\Users\\Asus\\Documents\\Vendolista\\home_depot_data.csv"
path = "C:\\Users\\Asus\\Documents\\Vendolista\\home depot"
# csv_loader = CSVLoader(file_path=path, encoding='utf-8')
csv_loader = DirectoryLoader(path,
glob="**/*.csv",
show_progress=True,
use_multithreading=True,
silent_errors=True,
loader_cls = CSVLoader)
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
documents = csv_loader.load()
# text_splitter = RecursiveCharacterTextSplitter(
# chunk_size=200,
# chunk_overlap=50,
# )
# chunks = text_splitter.split_documents(documents)
chunks = documents
embeddings = OpenAIEmbeddings()
persist_directory = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
knowledge_base = Chroma(embedding_function=embeddings, persist_directory=persist_directory)
# # Split the chunks into smaller batches
# batch_size = 5000
# for i in range(0, len(chunks), batch_size):
# batch = chunks[i:i+batch_size]
# knowledge_base.add_documents(batch)
# Save the vector store to disk
knowledge_base.persist()
# Load the vector store from disk
knowledge_base = Chroma(chunks, persist_directory=persist_directory, embedding_function=embeddings)
class Product(BaseModel):
"""Product details schema."""
url:str = Field(description="Full URL link to the product webpage on Homedepot.")
title:str = Field(description="Title of the product.")
description:str = Field(description="Description of the prodcut.")
brand:str = Field(description="Manufacturing brand of the product.")
price:float = Field(description="Unit selling price of the product.")
parser = PydanticOutputParser(pydantic_object=Product)
question_template = """ Make sure you understand the question as its very important for the user.
You never know what situation they are in and you need to ensure that its understood very well but do not repeat
or rewrite the question
Input: {question}
"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(question_template)
# Chain for question generation
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
# Chat Prompt
system_template = """
You are a friendly, conversational retail shopping assistant named RAAFYA.
You will always and always and always only follow these set of rules and nothing else no matter what:
1) You will provide the user answers based on the csv file that you can only read from which is called "home_depot_data.csv"
2) You will never mention the name of the dataset that you have. Just say "my data" instead
3) Focus 100% to understand exactly what the customer is looking for and only give him whats available based on the data.
4) Do not get anything or say anything that is not related to the data that you have and never provide wrong information.
5) Use the following context including product name descriptions, and keywords to show the shopper whats available,
help find what they want, and answer their questions related to your job
6) Never ever consider or think or even mention that you do not have access to the internet because it is not your job
and it is not your task. I will repeat it again and again, your information is only and only coming from the dataset
that you have which is called "home_depot_data.csv" but you must not mention that to anyone for security purposes
7) Everyime you answer a question, write on a new line "is there anything else you would like me to help you with?"
8) If a customer asked for a product and it is not available then say "Sorry it is currently unavailable but you can
reach out to our staff and ask them about it at [email protected]"
9) If the person asked for more details then provide him the details based on the output parser that you have:
URL:
Title:
Description:
Brand:
Price:
Context:
{context}
"""
system_message_prompt = SystemMessagePromptTemplate.from_template(system_template)
# Human Prompt
human_template="""{format_instructions}
Question: {question}"""
# human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
# Inject instructions into the prompt template.
human_message_prompt = HumanMessagePromptTemplate(
prompt=PromptTemplate(
template=human_template,
input_variables=["question"],
partial_variables={"format_instructions": parser.get_format_instructions()}
)
)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# Chain for Q&A
answer_chain = load_qa_chain(llm,
chain_type="stuff",
prompt=chat_prompt)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
# Chain
qa = ConversationalRetrievalChain(
retriever = knowledge_base.as_retriever(),
question_generator = question_generator,
combine_docs_chain = answer_chain,
memory=memory,
rephrase_question=False,
return_source_documents=True
)
def main():
while True:
user_input = input("What would you like to shop for: ")
if user_input.lower() in ["exit"]:
break
if user_input != "":
with get_openai_callback() as cb:
result = qa({"question": user_input})
print()
# print(cb)
# print()
if __name__ == "__main__":
main()
|
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PR_kwDOIPDwls5e393Q
| 13,042 |
Add Proper Import Error
|
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[] | null | 1 | 2023-11-08T02:49:23 | 2023-11-08T18:29:09 | 2023-11-08T18:29:09 |
CONTRIBUTOR
| null |
- **Description:** The issue was not listing the proper import error for amazon textract loader.
- **Issue:** Time wasted trying to figure out what to install... (langchain docs don't list the dependency either)
- **Dependencies:** N/A
- **Tag maintainer:** @sbusso
- **Twitter handle:** @h9ste
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PR_kwDOIPDwls5e31bN
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Add template for conversational rag with timescale vector
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CONTRIBUTOR
| null |
**Description:** This is like the rag-conversation template in many ways. What's different is:
- support for a timescale vector store.
- support for time-based filters.
- support for metadata filters.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5e3zZL
| 13,040 |
redirect ls walkthrough
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[] | null | 1 | 2023-11-08T01:58:27 | 2023-11-08T02:24:14 | 2023-11-08T02:24:13 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5e3wEU
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Azure OpenAI Embeddings
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COLLABORATOR
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PR_kwDOIPDwls5e3v0e
| 13,038 |
Fleet context
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[] | null | 1 | 2023-11-08T01:40:59 | 2023-11-08T02:57:10 | 2023-11-08T02:57:09 |
COLLABORATOR
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cc @adrwz
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fastllm model
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[] | null | 1 | 2023-11-08T01:06:34 | 2023-11-08T01:06:56 | null |
NONE
| null |
### Issue you'd like to raise.
Langchain.llm matches the vllm model, can it also match the fastllm model？
### Suggestion:
_No response_
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I_kwDOIPDwls52KsyX
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Step-Back Prompting
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[] | null | 4 | 2023-11-08T00:58:46 | 2023-11-08T16:56:36 | 2023-11-08T16:46:08 |
COLLABORATOR
| null |
### Feature request
[This idea](https://arxiv.org/abs/2310.06117) is promising and it can be implemented with LangChain
### Motivation
To add a new chaining technique.
### Your contribution
I'm not sure I can implement it.
|
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I_kwDOIPDwls52KqnQ
| 13,035 |
Chain-of-Verification
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[] | null | 7 | 2023-11-08T00:48:40 | 2023-11-10T19:06:01 | null |
COLLABORATOR
| null |
### Feature request
[Chain-of-Verification](https://arxiv.org/pdf/2309.11495.pdf) looks like an interesting idea and LangChain can implement it.
### Motivation
to add one more effective method of the chaining.
### Your contribution
Not sure I can implement it. :(
|
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PR_kwDOIPDwls5e3hee
| 13,034 |
cli updates, 0.0.16
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[] | null | 1 | 2023-11-08T00:31:31 | 2023-11-08T15:47:31 | 2023-11-08T15:47:30 |
COLLABORATOR
| null |
- confirm flags, serve detection
- 0.0.16
- always gen code
- pip bool
|
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PR_kwDOIPDwls5e3WBg
| 13,033 |
Agent window management how to
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[] | null | 1 | 2023-11-07T23:42:31 | 2023-11-15T17:38:04 | 2023-11-15T17:38:02 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5e3QiW
| 13,032 |
Docusaurus 3
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[] | null | 1 | 2023-11-07T23:22:16 | 2023-11-29T18:21:49 | 2023-11-29T18:21:49 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5e3QG1
| 13,031 |
Upgrade docs postcss
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5e3OSL
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template versions again
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COLLABORATOR
| null |
- scipy was locked due to py version
- same guardrails-output-parser
- rag-redis
|
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Update Deps in Experimental
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PR_kwDOIPDwls5e3LJ9
| 13,028 |
Relock Templates
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[] | null | 1 | 2023-11-07T22:58:02 | 2023-11-07T23:01:50 | 2023-11-07T23:01:49 |
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| 1,982,332,029 |
I_kwDOIPDwls52J_x9
| 13,027 |
Compatibility Issue with OpenAI Python Package (Version 1.1.1)
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[] | null | 14 | 2023-11-07T22:18:36 | 2023-12-14T11:47:23 | null |
NONE
| null |
### Description
Compatibility issue with the Langchain library due to the recent changes in the OpenAI Python package (version 1.1.1). The Langchain library relies on certain structures and imports from the OpenAI package, which have been modified in the new version. Specifically, the issue seems to be related to the following changes:
- In the Langchain code, the error handling imports in [langchain/llms/openai.py](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/openai.py#L83 ) at line 90 were based on the older structure of the OpenAI package. In the newer version, these imports have been restructured and are available in openai._exceptions.
- In [langchain/llms/openai.py](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/openai.py#L240 ) at line 266, values["client"] = openai.Completion is no longer valid in the new version of OpenAI (version 1.1.1).
```
!pip install langchain openai
from langchain import OpenAI
import os
os.environ["OPENAI_API_KEY"] = "key"
llm = OpenAI(
model_name="text-davinci-003",
temperature= 0.2,
max_tokens= 64,
openai_api_key=os.environ["OPENAI_API_KEY"],
)
```
Also
**Note:**
To avoid the above error, users should downgrade the OpenAI package to version 0.28.1.
### 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
!pip install langchain openai
from langchain import OpenAI
import os
os.environ["OPENAI_API_KEY"] = "key"
llm = OpenAI(
model_name="text-davinci-003",
temperature= 0.2,
max_tokens= 64,
openai_api_key=os.environ["OPENAI_API_KEY"],
)
### Expected behavior
Langchain should work without errors when using OpenAI version 1.1.1.
|
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| 1,982,276,784 |
I_kwDOIPDwls52JySw
| 13,026 |
Embedding on Multi-GPU
|
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[] | null | 2 | 2023-11-07T21:39:50 | 2023-11-07T21:49:49 | null |
NONE
| null |
### Issue you'd like to raise.
How can I run embedding with "gte-large" on a multi-GPU machine?
Trying per issue #3486
```
model_name = "thenlper/gte-large"
encode_kwargs = {"normalize_embeddings": True}
model_kwargs = {"device": "cuda", "multi_process":True}
hf = HuggingFaceBgeEmbeddings(
model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs,
)
embedding = hf.embed_query("hi this is harrison")
len(embedding)
```
uses a single GPU
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| 1,982,234,376 |
I_kwDOIPDwls52Jn8I
| 13,024 |
[Loader template] Unstructured pdf partition to vectorstore
|
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[] | null | 1 | 2023-11-07T21:11:05 | 2023-11-07T21:11:28 | null |
COLLABORATOR
| null |
### Feature request
Load complex PDFs similar to:
https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb
### Motivation
RAG apps that need complex data loading
### Your contribution
Add template
|
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| 1,982,215,271 |
PR_kwDOIPDwls5e2kie
| 13,022 |
added system prompt and template fields to ollama
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CONTRIBUTOR
| null |
**Description**
the ollama api now supports passing system prompt and template directly instead of modifying the model file , but the ollama integration in langchain did not have this change updated . The update just adds these two parameters to it ( there are 2 more parameters that are pending to be updated, I was not sure about their utility wrt to langchain )
Refer : https://github.com/jmorganca/ollama/commit/8713ac23a86e1eaf8b94f93bb0d51d13b0225d87
**Issue** : None Applicable
**Dependencies** : None Changed
**Twitter handle** : https://twitter.com/violetto96
|
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PR_kwDOIPDwls5e2cqJ
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update stack diagram
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[] | null | 1 | 2023-11-07T20:35:48 | 2023-11-07T20:59:25 | 2023-11-07T20:59:24 |
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feat: Vertex AI Search - Add Snippet Retrieval for Non-Advanced Website Data Stores
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[] | null | 2 | 2023-11-07T20:32:49 | 2023-11-09T02:52:51 | 2023-11-09T02:52:51 |
CONTRIBUTOR
| null |
https://cloud.google.com/generative-ai-app-builder/docs/snippets#snippets
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