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https://api.github.com/repos/langchain-ai/langchain/issues/15347 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15347/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15347/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15347/events | https://github.com/langchain-ai/langchain/issues/15347 | 2,060,810,975 | I_kwDOIPDwls561Xrf | 15,347 | DOC: Summarization 'map_reduce' - Can't load tokenizer for 'gpt2' | {
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] | open | false | null | [] | null | 4 | 2023-12-30T17:44:16 | 2024-01-04T15:35:08 | null | NONE | null | ### Issue with current documentation:
The [documentation](https://python.langchain.com/docs/use_cases/summarization) describes the different options for summarizing a text, for longer texts the 'map_reduce' option is suggested. It is mentioned further under 'Go deeper' that it is possible to use different LLMs via the `llm` parameter. This seems to work well using the code below with the `chain_type='stuff'` parameter and, in particular, using a local model (in the example below [this model](https://huggingface.co./TheBloke/Llama-2-7B-Chat-GGUF) is used).
```
from langchain.document_loaders import PyPDFLoader
from langchain.llms import CTransformers
from langchain.chains.summarize import load_summarize_chain
# load a PDF-file
loader = PyPDFLoader("C:/xyz.pdf")
docs = loader.load()
# use a local LLAMA2 model
llm = CTransformers(model='./models/llama-2-7b-chat.Q5_K_M.gguf', model_type='llama', config={'context_length': 4096, 'max_new_tokens': 256, 'temperature': 0}, local_files_only=True)
# summarise the text (this works only if it fits into the context length of ~4000 tokens)
chain = load_summarize_chain(llm, chain_type="stuff")
chain.run(docs)
```
However, surprisingly, it returns the following error when using the `chain_type='map_reduce'` parameter: 'OSError: Can't load tokenizer for 'gpt2'. If you were trying to load it from 'https://huggingface.co./models', make sure you don't have a local directory with the same name. Otherwise, make sure 'gpt2' is the correct path to a directory containing all relevant files for a GPT2TokenizerFast tokenizer.'
The suggestion [mentioned in this issue on Github](https://github.com/langchain-ai/langchain/issues/9273) doesn't work for the local model used above. It would be great to have more specific information in the LangChain documentation on (1) how to perform text summarization with LangChain using different LLMs, and (2) specifically for using local models that don't require an internet connection and/or require gpt2. Since the above code works with the parameter `chain_type='stuff'` but not with the parameter `chain_type='map_reduce'`, it would be important to explain what happens under the hood so users can make this work for local models.
### Idea or request for content:
_No response_ | {
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] | open | false | null | [] | null | 2 | 2023-12-30T15:47:22 | 2023-12-30T16:27:47 | null | NONE | null | ### Issue you'd like to raise.
Help me understand how I can save the intermediate data of chain execution results?
### Suggestion:
_No response_ | {
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] | null | 12 | 2023-12-30T15:04:38 | 2024-01-18T04:25:24 | null | NONE | null | **Description:** Gemini model has quite annoying default safety_settings settings. In addition, current VertexAI class doesn't provide a property to override such settings.
So, this PR aims to
- add safety_settings property to VertexAI
- fix issue with incorrect LLM output parsing when LLM responds with appropriate 'blocked' response
- fix issue with incorrect parsing LLM output when Gemini API blocks prompt itself as inappropriate
- add safety_settings related tests
I'm not enough familiar with langchain code base and guidelines. So, any comments and/or suggestions are very welcome.
**Issue:** it will likely fix #14841
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] | open | false | null | [] | null | 3 | 2023-12-30T11:14:48 | 2024-01-07T20:59:57 | null | NONE | null | ### Issue with current documentation:
A few days back, I was referring to the [Prompt templates](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/) page which now shows: "**Page Not Found**"
### Idea or request for content:
I understand that LangChain is an evolving framework undergoing continuous development.
- Could we consider implementing versioning for the documentation? This would allow users to access specific documentation versions.
- Alternatively, if a section undergoes modification, we could preserve the existing documentation and label it as 'Legacy,' ensuring clarity about deprecated practices. | {
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] | closed | false | null | [] | null | 1 | 2023-12-30T10:29:06 | 2024-01-15T19:59:40 | 2024-01-15T19:59:40 | NONE | null | ### System Info
LangChain 0.0.353
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Cannot set top_p to useful values via ChatOllama(top_p=0.3), is reduced to value 0 because it's an int:
top_p: Optional[int] = None
top_p must be float.
### Expected behavior
top_p must be a float, 0.3 should appear in ollama log. | {
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] | open | false | null | [] | null | 7 | 2023-12-30T04:32:17 | 2023-12-30T05:26:39 | null | NONE | null | ### Issue you'd like to raise.
`below is my code for generating custom prompt which takes context and user query and we pass it into model:
def generate_custom_prompt(new_project_qa,query,name,not_uuid):
check = query.lower()
result = new_project_qa(query)
relevant_document = result['source_documents']
context_text="\n\n---\n\n".join([doc.page_content for doc in relevant_document])
# print(context_text,"context_text")
user_experience_inst = UserExperience.objects.get(not_uuid=not_uuid)
greetings = ['hi', 'hello', 'hey', 'hui', 'hiiii', 'hii', 'hiii', 'heyyy']
if check in greetings:
custom_prompt_template = f"""
Just simply reply with "Hello {name}! How can I assist you today?"
"""
elif check not in greetings and user_experience_inst.custom_prompt:
custom_prompt_template = f"""Answer the question based only on following context: ```{context_text} ```
You are a chatbot designed to provide answers to User's Questions:```{check}```, delimited by triple backticks.
Generate your answer to match the user's requirements: {user_experience_inst.custom_prompt}
If you encounter a question for which you don't know the answer, please respond with 'I don't know' and refrain from making up an answer.
- Before saying 'I don't know,' please re-verify your vector store to ensure the answer is not present in the database.
Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, feel free to ask for clarification.
User's Question: ```{check}```
AI Answer:"""
else:
# Create the custom prompt template
custom_prompt_template = f"""Generate your response exclusively from the provided context: {{context_text}}. You function as a chatbot specializing in delivering detailed answers to the User's Question: ```{{check}} ```, enclosed within triple backticks.
Generate your answer in points in the following format:
1. Point no 1
1.1 Its subpoint in details
1.2 More information if needed.
2. Point no 2
2.1 Its subpoint in details
2.2 More information if needed.
…
N. Another main point.
If you encounter a question for which you don't know the answer based on the predefined points, please respond with 'I don't know' and refrain from making up an answer.
However, if the answer is not present in the predefined points, then Provide comprehensive information related to the user's query.
Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, you can ask for clarification.
User's Question: ```{{check}} ```
AI Answer:"""
# Create the PromptTemplate
custom_prompt = ChatPromptTemplate.from_template(custom_prompt_template)
formatted_prompt = custom_prompt.format(context_text=context_text, check=check)
return formatted_prompt
below is my conversation chain where i am inplementing memory
def retreival_qa_chain(chroma_db_path):
embedding = OpenAIEmbeddings()
vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding)
llm = ChatOpenAI(temperature=0.1)
memory = ConversationBufferMemory(llm=llm,output_key='answer',memory_key='chat_history',return_messages=True)
retriever = vectordb.as_retriever(search_kwargs={"k": 2})
qa = ConversationalRetrievalChain.from_llm(llm=llm,memory=memory,chain_type="stuff",retriever=retriever,return_source_documents=True,get_chat_history=lambda h : h,verbose=True)
# qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever,return_source_documents=True)
return qa
but I am not getting desired output as expected `
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15338 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15338/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15338/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15338/events | https://github.com/langchain-ai/langchain/pull/15338 | 2,060,502,197 | PR_kwDOIPDwls5i9T4o | 15,338 | <langchain>: add checks for qaSourcesChain to check if the returned answer referen… | {
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] | closed | false | null | [] | null | 2 | 2023-12-30T02:09:07 | 2024-01-01T21:34:50 | 2024-01-01T21:34:50 | NONE | null | …ces the source from which it returning an answer
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** when using QAWithSourcesChain I realized many times that the language model would return results which was not in the source being referenced. The example in this PR checks if the LLM refers to the actual source by mentioning its name. For example, if you ask an LLM tell me something from Book A the LLM must mention something from Book A. The implementation presented is preliminary and has to further developed but I want your general thoughts on the matter :) ,
- **Issue:** hallucination of an answer which is not grounded in the source by the chain,
- **Dependencies:** None,
- **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` from the root of the package you've modified to check this locally.
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If you're adding a new integration, please include:
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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https://api.github.com/repos/langchain-ai/langchain/issues/15337 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15337/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15337/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15337/events | https://github.com/langchain-ai/langchain/pull/15337 | 2,060,487,247 | PR_kwDOIPDwls5i9ROw | 15,337 | Langchain: FAISS Translator for Self Query Retriver | {
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**Description:** Issue #15331 pointed out that there was no support for FAISS for `SelfQueryRetriever`. Therefore, I decided to add the desired support. I understand that FAISS does not have an internal query language like other vector stores (e.g., Chroma), but I noticed that the current implementation of the `FAISS` VectorStore Class in Langchain allows the user to filter documents based on given metadata attributes. Therefore, the translator simply translates the structured query according to the logic of filtering defined in `similarity_search_with_score_by_vector` and `max_marginal_relevance_search_with_score_by_vector`, which filters the documents for the specific values present in metadata keys.
**Issue:** #15331
PS: I have not added the docs for this one. Only once you see the value in adding this translator, I will add the docs. | {
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<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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] | closed | false | null | [] | null | 5 | 2023-12-30T00:41:30 | 2024-01-02T23:52:43 | 2024-01-02T23:52:42 | COLLABORATOR | null | - created `language_community.memory`
- moved zep and motorhed from `langchain.memory` to `language_community.memory`
- created `language_core.memory`
- moved several base classes into `language_core.memory`
- moved `ChatMessageHistory` from `langchain_community` to `langchain_core`
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https://api.github.com/repos/langchain-ai/langchain/issues/15334 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15334/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15334/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15334/events | https://github.com/langchain-ai/langchain/pull/15334 | 2,060,466,339 | PR_kwDOIPDwls5i9NKr | 15,334 | Fetch runnable config from context var inside runnable lambda and runnable generator | {
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] | closed | false | null | [] | null | 1 | 2023-12-29T23:52:44 | 2024-01-02T20:16:40 | 2024-01-02T20:16:39 | COLLABORATOR | null | - easier to write custom logic/loops with automatic tracing
- if you don't want to streaming support write a regular function and pass to RunnableLambda
- if you do want streaming write a generator and pass it to RunnableGenerator
```py
import json
from typing import AsyncIterator
from langchain_core.messages import BaseMessage, FunctionMessage, HumanMessage
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import Runnable, RunnableGenerator, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.chat_models import ChatOpenAI
from langchain.tools.render import format_tool_to_openai_function
def _get_tavily():
from langchain.tools.tavily_search import TavilySearchResults
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
tavily_search = TavilySearchAPIWrapper()
return TavilySearchResults(api_wrapper=tavily_search)
async def _agent_executor_generator(
input: AsyncIterator[list[BaseMessage]],
*,
max_iterations: int = 10,
tools: dict[str, BaseTool],
agent: Runnable[list[BaseMessage], BaseMessage],
parser: Runnable[BaseMessage, AgentAction | AgentFinish],
) -> AsyncIterator[BaseMessage]:
messages = [m async for mm in input for m in mm]
for _ in range(max_iterations):
next_message = await agent.ainvoke(messages)
yield next_message
messages.append(next_message)
parsed = await parser.ainvoke(next_message)
if isinstance(parsed, AgentAction):
result = await tools[parsed.tool].ainvoke(parsed.tool_input)
next_message = FunctionMessage(name=parsed.tool, content=json.dumps(result))
yield next_message
messages.append(next_message)
elif isinstance(parsed, AgentFinish):
return
def get_agent_executor(tools: list[BaseTool], system_message: str):
llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_message),
MessagesPlaceholder(variable_name="messages"),
]
)
llm_with_tools = llm.bind(
functions=[format_tool_to_openai_function(t) for t in tools]
)
agent = {"messages": RunnablePassthrough()} | prompt | llm_with_tools
parser = OpenAIFunctionsAgentOutputParser()
executor = RunnableGenerator(_agent_executor_generator)
return executor.bind(
tools={tool.name for tool in tools}, agent=agent, parser=parser
)
agent = get_agent_executor([_get_tavily()], "You are a very nice agent!")
async def main():
async for message in agent.astream(
[HumanMessage(content="whats the weather in sf tomorrow?")]
):
print(message)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
results in this trace https://smith.langchain.com/public/fa17f05d-9724-4d08-8fa1-750f8fcd051b/r | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15333 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15333/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15333/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15333/events | https://github.com/langchain-ai/langchain/issues/15333 | 2,060,459,074 | I_kwDOIPDwls560BxC | 15,333 | This model's maximum context length is 4097 tokens, however you requested 4177 tokens | {
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] | open | false | null | [] | null | 1 | 2023-12-29T23:25:32 | 2023-12-29T23:32:56 | null | NONE | null | ### System Info
New versions
### 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
Start the code
### Expected behavior
Hi , I'm trying to do a chain stuff query, but sometimes when I ask questions I have this error:
openai.BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 4097 tokens, however you requested 4177 tokens (3921 in your prompt; 256 for the completion). Please reduce your prompt; or completion length.", 'type': 'invalid_request_error', 'param': None, 'code': None}}
How can I solve this ? Can I cut my prompt ? If yes how? Or Can I upgrade the max_token ? IF yes How please..
Here is my code:
import getpass
import os
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain_community.vectorstores import Pinecone
from langchain_community.embeddings.openai import OpenAIEmbeddings
import pinecone
import sys
# Set your Pinecone API key and environment
pinecone_api = "API"
pinecone_env = "API"
# Set your OpenAI API key
openai_api = "API"
# Initialize Pinecone
pinecone.init(api_key=pinecone_api, environment=pinecone_env)
# Define the index name
index_name = "rewind"
# Check if the index already exists, if not, create it
if index_name not in pinecone.list_indexes():
pinecone.create_index(name=index_name, metric="cosine", dimension=1536)
# Initialize the OpenAIEmbeddings
embeddings = OpenAIEmbeddings(api_key=openai_api)
# Create or load the Pinecone index
docsearch = Pinecone.from_existing_index(index_name, embeddings)
# Perform similarity search
query = sys.argv[1] if len(sys.argv) > 1 else "what Commits there is in github"
text_splitter = CharacterTextSplitter(chunk_size=3000, chunk_overlap=0)
docs = docsearch.similarity_search(query)
docs = text_splitter.split_documents(docs)
if __name__ == '__main__':
results = docsearch.similarity_search(query)
# Load the question answering chain
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
chain = load_qa_chain(OpenAI(), chain_type="stuff")
answers = chain.run(input_documents=docs, question=query)
print(answers) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15331 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15331/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15331/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15331/events | https://github.com/langchain-ai/langchain/issues/15331 | 2,060,431,327 | I_kwDOIPDwls56z6_f | 15,331 | Self query retriever with Vector Store type <class 'langchain_community.vectorstores.faiss.FAISS'> not supported. | {
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] | closed | false | null | [] | null | 4 | 2023-12-29T22:05:18 | 2024-01-11T22:59:30 | 2024-01-11T22:59:29 | NONE | null | ### System Info
I've been trying to create a self query retriever so that I can look at metadata field info. This issue comes up. Should I be using another vector store to make this work? I can only really work with FAISS. I cannot use ChromaDB since my Python environment is limited to a previous version.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a FAISS Vectorstore DB
2. Create a metadata_field_info object and pass it to a SelfQuery object
3. Create LLM with this retriever
```python
embedding_function = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY, model="text-embedding-ada-002")
db = FAISS.load_local(input_dir + "/" + "storage/deploy/faiss-db", embedding_function)#, distance_strategy="COSINE", normalize_L2 = True)
# retriever = KNNRetriever(vectorstore=db)
llm = ChatOpenAI(
temperature=0,
verbose=False,
openai_api_key=key,
model_name="gpt-3.5-turbo"
)
metadata_field_info = [
AttributeInfo(
name="source",
description="The document this chunk is from.",
type="string",
),
AttributeInfo(
name="origin",
description="The origin the document came from. Comes from either scraped websites like TheKinection.org, Kinecta.org or database files like Bancworks. Bancworks is the higher priority.",
type="string",
),
AttributeInfo(
name="date_day",
description="The day the document was uploaded.",
type="integer",
),
AttributeInfo(
name="date_month",
description="The month the document was uploaded.",
type="integer",
),
AttributeInfo(
name="date_year",
description="The year the document was uploaded.",
type="integer",
),
]
# retriever = db.as_retriever(search_type="similarity", search_kwargs={'k': 6}, metadata_field_info=metadata_field_info)
retriever = SelfQueryRetriever.from_llm(
llm, db, "Information about where documents originated from and when they were published.", metadata_field_info, verbose=True
)
```
### Expected behavior
Successfully create a SelfQuery retriever with FAISS vector store. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15330 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15330/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15330/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15330/events | https://github.com/langchain-ai/langchain/pull/15330 | 2,060,416,384 | PR_kwDOIPDwls5i9DDw | 15,330 | Community: Newlines before bullets in IPYNB files (Vectara) | {
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] | closed | false | null | [] | null | 1 | 2023-12-29T21:37:37 | 2023-12-30T22:04:04 | 2023-12-30T22:04:04 | CONTRIBUTOR | null | - **Description:** updated all Vectara IPYNB files so that bullets look okay in docs (added newline)
- **Twitter handle:** @ofermend | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15329 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15329/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15329/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15329/events | https://github.com/langchain-ai/langchain/pull/15329 | 2,060,386,705 | PR_kwDOIPDwls5i889s | 15,329 | Propagate context vars in all classes/methods | {
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] | open | false | null | [] | null | 4 | 2023-12-29T20:33:36 | 2023-12-30T00:22:08 | null | NONE | null | ### Feature request
This proposal requests the integration of the latest OpenAI models, specifically gpt-4-1106-preview, into the existing framework of [relevant GitHub project, e.g., LangChain]. The newer models offer significantly larger context windows, which are crucial for complex SQL querying and other advanced functionalities. This feature would involve ensuring compatibility with the latest version of the OpenAI API (version 1.0.0 and beyond), which has undergone substantial changes, including the deprecation of certain features like openai.ChatCompletion. Relevant links:
OpenAI API (1.0.0): [OpenAI API Documentation](https://github.com/openai/openai-python)
Migration Guide: [OpenAI Python Library Migration Guide](https://github.com/openai/openai-python/discussions/742)
### Motivation
The primary motivation for this feature request is to leverage the advanced capabilities of the newer OpenAI models, particularly the extended context windows they offer. These capabilities are essential for applications involving extensive data interaction and complex language understanding, such as SQL database querying and management.
Current limitations with the older models and API versions restrict the potential of applications, especially when dealing with lengthy queries or requiring deeper contextual understanding. For example, while working on a project involving the LangChain framework for SQL database interaction, I encountered the APIRemovedInV1 error, which signifies incompatibility with the latest OpenAI API. This issue underscores the need for updating the framework to align with the latest advancements in language models and API standards.
### Your contribution
Might make my own SQL Agent or modify yours. | {
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- **Issue:** https://github.com/langchain-ai/langchain/issues/14511
- **Dependencies:** none
- **Twitter handle:** none
Taken from this fix: https://github.com/gpt-engineer-org/gpt-engineer/issues/804#issuecomment-1769853850
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
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/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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https://api.github.com/repos/langchain-ai/langchain/issues/15325 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15325/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15325/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15325/events | https://github.com/langchain-ai/langchain/issues/15325 | 2,060,357,840 | I_kwDOIPDwls56zpDQ | 15,325 | DOC: how to use embeddings in langchain with fireworks? | {
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how to use embeddings in langchain with fireworks?(I need it for RAG) It's just that the documentation only talks about OpenAIEmbeddings
https://python.langchain.com/docs/modules/data_connection/text_embedding/
### Idea or request for content:
RAG with fireworks API | {
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- **Issue:** not applicable
- **Dependencies:** none
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https://api.github.com/repos/langchain-ai/langchain/issues/15323 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15323/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15323/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15323/events | https://github.com/langchain-ai/langchain/pull/15323 | 2,060,282,246 | PR_kwDOIPDwls5i8nZV | 15,323 | Harrison/new docs revamp | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15322 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15322/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15322/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15322/events | https://github.com/langchain-ai/langchain/pull/15322 | 2,060,256,173 | PR_kwDOIPDwls5i8iGE | 15,322 | langchain[patch]: Release 0.0.353 | {
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] | closed | false | null | [] | null | 1 | 2023-12-29T16:42:17 | 2023-12-29T19:53:31 | 2023-12-29T19:53:31 | CONTRIBUTOR | null | Correcting a small typo ('the' instead of 'then') and changing another 'the' (instead of 'then' too, it was a hard day for the 'n' key :D) to 'also' to match better with what is done in the code
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https://api.github.com/repos/langchain-ai/langchain/issues/15320 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15320/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15320/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15320/events | https://github.com/langchain-ai/langchain/pull/15320 | 2,060,247,897 | PR_kwDOIPDwls5i8gUg | 15,320 | community[patch]: Release 0.0.7 | {
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] | closed | false | null | [] | null | 2 | 2023-12-29T15:48:07 | 2023-12-29T16:03:41 | 2023-12-29T16:03:41 | NONE | null | ### System Info
"langchain": "^0.0.211",
MacOS Sonoma 14.2
Next.js 14.0.4
### Who can help?
@agola11
@hwc
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. pnpm add langchain in a Next.js project
2. Create a Next.js Route handler
3. Create the following route:
```
import { NextResponse } from 'next/server';
import { ChatOllama } from 'langchain/chat_models/ollama';
import { ChatPromptTemplate, MessagesPlaceholder } from 'langchain/prompts';
import { BufferMemory, ChatMessageHistory } from 'langchain/memory';
import { ConversationChain } from 'langchain/chains';
export async function POST(req: Request) {
const data = await req.json();
const prompt = ChatPromptTemplate.fromMessages([
[
'system',
`You are an AI Computer Science Data Structures teaching system that responds to all questions STRICTLY
in JSON format. You will be given a question on DSA concepts. Contents of JSON made by you will be used
to create elements within a node of a graph that displays
explanations of topics, and a user interface that allows users to follow up if they need help or want
more information. There are 4 elements, "Topic", "Description", "Subtopics", "Questions": an array of strings. You will also be given a number of nodes that already
exist, to be able to assign unique ids. IDs MUST BE STRINGS. MAKE SURE YOU ARE ONLY REPLYING WITH JSON AND NOT MARKDOWN
These are the only node types you are allowed to pick from:
"promptNode": USE FOR ALL EXPLANATIONS
"confusedNode": USED WHEN CONFUSED
{
"{DEFINE ID BUT IN "STRING" FORM! +1 HIGHER THAN NUMBER GIVEN}": {
"THE ID AGAIN": {number},
"type": "promptNode",
"position": { "x": 0, "y": 0 },
"data": {
"topic": "{Short name of topic}",
"description": "{The explanation of topic}",
"subtopics": [an array of strings of 5 related topics],
"questions": [an array of objects of 4 related questions and answers, eg: {'q': 'Question?', 'a': 'Ans'}],
"im_confused": [array of concepts mentioned in the description that they could be confused about]
}
}`,
],
new MessagesPlaceholder('history'),
['human', '{input}'],
]);
//@ts-ignore
const chatHistory = [];
const llm = new ChatOllama({
baseUrl: 'http://localhost:11434', // Default value
model: 'mistral', // Default value
});
const memory = new BufferMemory({
returnMessages: true,
memoryKey: 'history',
//@ts-ignore
chatHistory: new ChatMessageHistory(chatHistory),
});
const chain = new ConversationChain({
memory: memory,
prompt: prompt,
llm: llm,
verbose: true,
});
const result = await chain.invoke({
input: data.prompt,
});
console.log(result);
return NextResponse.json(
{
},
{ status: 200 }
);
}
```
### Expected behavior
Model output. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15317 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15317/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15317/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15317/events | https://github.com/langchain-ai/langchain/issues/15317 | 2,059,715,813 | I_kwDOIPDwls56xMTl | 15,317 | Issue: Error Handling in Tools used in custom agents | {
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] | open | false | null | [] | null | 1 | 2023-12-29T12:44:32 | 2023-12-29T12:53:47 | null | NONE | null | ### Issue you'd like to raise.
I have built a custom LLM Agent by following the Documentation provided. The custom agent contains multiple tools, one of them is the "LLMMathChain" which is giving me ValueError, cause my agent is passing "None" as an Action Input. I want to handle that error. So that my chatbot doesn't break in the middle of a conversation.
## My Custom Agent
## Calculator Tool
## Prompt Template
## Output Parser
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15316 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15316/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15316/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15316/events | https://github.com/langchain-ai/langchain/issues/15316 | 2,059,700,790 | I_kwDOIPDwls56xIo2 | 15,316 | Add common mulit model support | {
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] | open | false | null | [] | null | 3 | 2023-12-29T12:42:22 | 2024-01-01T09:42:37 | null | NONE | null | ### Feature request
Presently, JSON can be utilized to enable the multimodal capability of GPT-4 series models within ChatOpenAI and OpenAI. However, this functionality lacks portability.
### Motivation
Using multimodal approaches lacks portability, and GPT-4 isn't the sole model employing multimodal capabilities. Therefore, it becomes imperative to establish a standardized method for accessing various multimodal models.
### Your contribution
I may submit a PR about this if I have spare time | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15315 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15315/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15315/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15315/events | https://github.com/langchain-ai/langchain/issues/15315 | 2,059,344,749 | I_kwDOIPDwls56vxtt | 15,315 | Issue: How can I implement Conversation Chain along with ConversationSummaryMemory | {
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] | open | false | null | [] | null | 1 | 2023-12-29T11:23:25 | 2023-12-29T11:31:41 | null | NONE | null | ### Issue you'd like to raise.
below is my code, How can I implement Conversation Chain along with ConversationSummaryMemory in my code
`def retreival_qa_chain(chroma_db_path):
embedding = OpenAIEmbeddings()
vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding)
llm = ChatOpenAI(temperature=0.1)
retriever = vectordb.as_retriever(search_kwargs={"k": 2})
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever,return_source_documents=True)
return qa
def create_global_qa_chain():
chroma_db_path = "chroma-databases"
folders = os.listdir(chroma_db_path)
qa_chains = {}
for index, folder in enumerate(folders):
folder_path = f"{chroma_db_path}/{folder}"
project = retreival_qa_chain(folder_path)
qa_chains[folder] = project
return qa_chains`
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15314 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15314/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15314/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15314/events | https://github.com/langchain-ai/langchain/issues/15314 | 2,059,302,480 | I_kwDOIPDwls56vnZQ | 15,314 | Issue: document_variable_name context was not found in llm_chain input_variables | {
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] | open | false | null | [] | null | 1 | 2023-12-29T10:42:37 | 2023-12-29T10:52:37 | null | NONE | null | ### Issue you'd like to raise.
I am trying to add a specific prompt template to my ConversationalRetrievalChain. This is my current code:
> PROMPT_TEMPLATE = """
Act as the policies interactive Bot that gives advice on the Company policies, Travel policies, and Information security policies for the company.
Do not try to make up an answer. Use only the given pieces of context; do not use your own knowledge.
Chat History:
{chat_history}
Follow Up Input: {question}
"""
qa_prompt = PromptTemplate(input_variables=["chat_history", "question",], template=PROMPT_TEMPLATE)
> chat = ChatOpenAI(
verbose=True,
model_name=MODEl_NAME,
temperature=TEMPERATURE,
max_retries=MAX_RETRIES,
streaming=True,
)
qa_chain =ConversationalRetrievalChain.from_llm(
llm=chat,
retriever=MyVectorStoreRetriever(
vectorstore=vectordb,
search_type="similarity_score_threshold",
search_kwargs={"score_threshold": SIMILARITY_THRESHOLD, "k": 1},
),
return_source_documents=True,
combine_docs_chain_kwargs={'prompt': qa_prompt}, )
response = qa_chain(
{
"question": query,
"chat_history": chat_history,
},
callbacks=[stream_handler],
)
This is the error I'm currently getting,
> qa_chain =ConversationalRetrievalChain.from_llm(
File "/home/sfm/anaconda3/envs/chat_v2/lib/python3.10/site-packages/langchain/chains/conversational_retrieval/base.py", line 360, in from_llm
doc_chain = load_qa_chain(
File "/home/sfm/anaconda3/envs/chat_v2/lib/python3.10/site-packages/langchain/chains/question_answering/__init__.py", line 249, in load_qa_chain
return loader_mapping[chain_type](
File "/home/sfm/anaconda3/envs/chat_v2/lib/python3.10/site-packages/langchain/chains/question_answering/__init__.py", line 81, in _load_stuff_chain
return StuffDocumentsChain(
File "/home/sfm/anaconda3/envs/chat_v2/lib/python3.10/site-packages/langchain/load/serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for StuffDocumentsChain
__root__
document_variable_name context was not found in llm_chain input_variables: ['chat_history', 'question'] (type=value_error)
can you help me to figure out the error and correct it?
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/15311 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15311/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15311/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15311/events | https://github.com/langchain-ai/langchain/pull/15311 | 2,059,260,313 | PR_kwDOIPDwls5i7ZVy | 15,311 | Feat: support RunnableBranch constructed with `|` | {
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] | closed | false | null | [] | null | 2 | 2023-12-29T09:55:58 | 2024-01-02T02:34:14 | 2024-01-01T22:25:59 | CONTRIBUTOR | null | ```python
from langchain_core.runnables import RunnableBranch
branch = RunnableBranch(
(lambda x: isinstance(x, str), lambda x: x.upper()),
(lambda x: isinstance(x, int), lambda x: x + 1),
(lambda x: isinstance(x, float), lambda x: x * 2),
lambda x: "goodbye",
)
branch.invoke("hello") # "HELLO"
branch.invoke(None) # "goodbye"
# A RunnableBranch constructed using the `|` operator
branch = branch | (lambda x: isinstance(x, bool), lambda x: f'{x}')
branch.invoke(true) # "true"
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15310 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15310/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15310/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15310/events | https://github.com/langchain-ai/langchain/issues/15310 | 2,059,251,491 | I_kwDOIPDwls56va8j | 15,310 | Slow aadd_documents using Qdrant and HuggingfaceEmbeddings on CPU | {
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] | open | false | null | [] | null | 1 | 2023-12-29T09:45:06 | 2023-12-29T09:52:34 | null | NONE | null | ### System Info
lc: 0.0.352, os: ubuntu 22, python 3.10
### Who can help?
### Description
I am encountering a significant performance issue when using Qdrant with HuggingfaceEmbeddings in a CPU-only environment, specifically within a FastAPI endpoint. The process is notably slow, particularly at the `aadd_documents(...)` stage.
### Additional Information
- As a comparison, I tried embedding a document directly using `sentence_transformers`. This approach utilized all CPU cores, resulting in a much faster process.
- I also experimented with a custom implementation, using only necessary functions from [this Qdrant file](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/vectorstores/qdrant.py) to perform `aadd_documents`. This approach also showed improved performance and full CPU utilization.
### Question
Does anyone have an idea or suggestion on what might be causing this performance bottleneck when using Qdrant with HuggingfaceEmbeddings in a CPU-only environment?
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
### Steps to Reproduce
1. Set up Qdrant with HuggingfaceEmbeddings in a CPU-only machine (no GPU).
2. Integrate it within a FastAPI endpoint.
3. Execute `aadd_documents(...)` for documents (for example, documents with around 45K characters).
### Expected behavior
### Expected Behavior
I expected the embedding and addition of documents to Qdrant to be efficient and utilize multiple CPU cores effectively.
### Observed Behavior
- The embedding process for a document of approximately 45K characters took over one minute.
- Resource utilization monitoring showed that only one out of 70 CPU cores was being utilized during the embedding process.
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] | open | false | null | [] | null | 4 | 2023-12-29T08:00:29 | 2024-01-01T01:34:14 | null | NONE | null | ### System Info
langchain 0.0.340
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
My vector library preserves tens of thousands of documents, but as the document increases, the accuracy rate of the retriever is becoming low, and the correct document cannot be retrieved.
The retrieval cannot be given the document correctly
### Expected behavior
db = FAISS.load_local(VS['comixfaiss'], embeddings)
retriever = db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.5,"k":5})
logger.info(retriever.get_relevant_documents('3736085'))
LOG:
2023-12-29 15:44:51,650 - loader.py[line:56] - INFO: Successfully loaded faiss with AVX2 support.
2023-12-29 15:44:52,843 - local_doc_qa.py[line:204] - INFO: [Document(page_content='\ufeffSelection: 非校验\n商品编号: 3730559\n商品名称: 飞捷 FJ21325 39-45码（QXGZ）中筒 防水鞋户外雨靴套鞋胶鞋 黑色(单位：双）\n物料编码: 3730559\n大类: 生活用品\n中类: 办公日杂\n小类: 雨伞雨具\n品牌: 梦奇\n颜色: 黑色\n型号: FJ21325\n建议零售价: 77.45\n卖点: 品牌：飞捷 颜色：黑色 型号：FJ21325 包装清单：雨靴*1\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 8975}), Document(page_content='\ufeffSelection: 非校验\n商品编号: 3051396\n商品名称: 得力 9387 三联送(销)货单据 129*188mm 20份/本 黄色 单位：本\n物料编码: 3051396\n大类: 办公文具\n中类: 财务行政用品\n小类: 财务单据\n品牌: 得力\n颜色: 黄色\n型号: 9387\n建议零售价: 4.35\n卖点: 0\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 20709}), Document(page_content='\ufeffSelection: 非校验\n商品编号: 3278812\n商品名称: 惠普\xa0W9055MC\xa0成像鼓 彩色 （单位：个）\n物料编码: 3278812\n大类: 办公耗材\n中类: 打印机耗材\n小类: 硒鼓\n品牌: 惠普\n颜色: 彩色\n型号: W9055MC\n建议零售价: 3645.88\n卖点: 打印机耗材\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 12167}), Document(page_content='\ufeffSelection: 非校验\n商品编号: 3197277\n商品名称: 得力 9307 报刊架 480*360*1450 银色 单位：个\n物料编码: 3197277\n大类: 办公文具\n中类: 会议展示用品\n小类: 报刊/杂志架\n品牌: 得力\n颜色: \n型号: 9307\n建议零售价: 330.33\n卖点: 0\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 16685}), Document(page_content='\ufeffSelection: 非校验\n商品编号: 3278811\n商品名称: 惠普 W9054MC 成像鼓 黑色 （单位：个）\n物料编码: 3278811\n大类: 办公耗材\n中类: 打印机耗材\n小类: 硒鼓\n品牌: 惠普\n颜色: 黑色\n型号: W9054MC\n建议零售价: 2471.19\n卖点: 打印机耗材\n上架状态: 上架\n状态: \n状态信息:', metadata={'source': '/mnt/data/pdf/comixgpt/pd/齐心商城商品数据2万条2023-12-27.csv', 'row': 12168})]
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support load any github file content based on file extension.
Why not use [git loader](https://python.langchain.com/docs/integrations/document_loaders/git#load-existing-repository-from-disk) ?
git loader clones the whole repo even only interested part of files, that's too heavy. This GithubFileLoader only downloads that you are interested files.
### Twitter handle
my twitter: @shufanhaotop
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https://api.github.com/repos/langchain-ai/langchain/issues/15304 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15304/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15304/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15304/events | https://github.com/langchain-ai/langchain/issues/15304 | 2,059,159,495 | I_kwDOIPDwls56vEfH | 15,304 | How do i use similarity caching in my code? | {
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] | open | false | null | [] | null | 1 | 2023-12-29T07:36:10 | 2023-12-29T07:44:03 | null | NONE | null | Hi @dosu-bot,
This is my code
```
import langchain
from langchain.cache import SQLAlchemyCache, Emb
from sqlalchemy import create_engine
from sqlalchemy.orm import declarative_base
from sqlalchemy import Column, Integer, Text
from urllib.parse import quote_plus
from langchain.llms import OpenAI
Base = declarative_base()
class FulltextLLMCache(Base):
__tablename__ = "llm_cache_full_text"
id = Column(Integer, primary_key=True)
prompt = Column(Text, nullable=False)
llm = Column(Text, nullable=False)
idx = Column(Integer)
response = Column(Text)
db_uri = f"mssql+pyodbc://JUPYTER\SQLEXPRESS/my_database?driver=ODBC+Driver+17+for+SQL Server"
cache_engine = create_engine(db_uri, pool_recycle=240, pool_size=20, max_overflow=30)
# Assigning to llm_cache
langchain.llm_cache = SQLAlchemyCache(cache_engine, FulltextLLMCache)
```
The above code is for exact cache which is very low hit rate, how can i do similarity caching?
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https://api.github.com/repos/langchain-ai/langchain/issues/15303 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15303/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15303/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15303/events | https://github.com/langchain-ai/langchain/issues/15303 | 2,059,118,347 | I_kwDOIPDwls56u6cL | 15,303 | Cache not getting saved in ConversationalRetrievalChain | {
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] | open | false | null | [] | null | 1 | 2023-12-29T06:30:14 | 2023-12-29T06:40:07 | null | NONE | null | Hi @dosu-bot.
Below is my code,
```
from langchain.cache import SQLAlchemyCache
from sqlalchemy import create_engine
engine = create_engine("mssql+pyodbc://JUPYTER\SQLEXPRESS/my_database?driver=ODBC+Driver+17+for+SQL Server")
set_llm_cache(SQLAlchemyCache(engine))
memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history", chat_memory=chat_message_history ,return_messages=True, output_key="answer", input_key="question")
retriever = load_emdeddings(cfg.faiss_persist_directory, cfg.embeddings).as_retriever(search_type="similarity_score_threshold",
search_kwargs={"score_threshold": .65,
"k": 2})
custom_prompt_template = """
You are a friendly chatbot named "XYZ", designed to provide assistance and answer queries.
{context}
Chat History: {chat_history}
Question: {question}
"""
# Create a PromptTemplate instance with your custom template
custom_prompt = PromptTemplate(
template=custom_prompt_template,
input_variables=["context", "question", "chat_history", "User_Name", "User_Location"],
)
# Use your custom prompt when creating the ConversationalRetrievalChain
qa = ConversationalRetrievalChain.from_llm(
llm,
verbose=False,
retriever=retriever,
memory=memory,
combine_docs_chain_kwargs={"prompt": custom_prompt},
return_source_documents = True
)
```
if i use llm.predict("Tell me a joke"). I can see the cache is getting stored in db.
but for qa if i ask question its not saving , why?
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https://api.github.com/repos/langchain-ai/langchain/issues/15302 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15302/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15302/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15302/events | https://github.com/langchain-ai/langchain/pull/15302 | 2,059,027,976 | PR_kwDOIPDwls5i6oK8 | 15,302 | Feat: support OpenAIAssistantRunnable async | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15301 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15301/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15301/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15301/events | https://github.com/langchain-ai/langchain/pull/15301 | 2,059,011,980 | PR_kwDOIPDwls5i6k8M | 15,301 | Patch: improve check openai version | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15300 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15300/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15300/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15300/events | https://github.com/langchain-ai/langchain/issues/15300 | 2,059,008,076 | I_kwDOIPDwls56ufhM | 15,300 | Using chipper model with hi_res strategy gives an error | {
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] | open | false | null | [] | null | 2 | 2023-12-29T02:33:48 | 2023-12-29T08:59:31 | null | NONE | null | ### System Info
Hi,
I'm new to this, so I apologize if my lack of in-depth understanding to how this library works caused to me raise a false alarm. Im trying to an ocr on pdf image using the UnstructuredPDFLoader, Im passing the following args:
`
loader = UnstructuredPDFLoader(file_path="myfile.pdf", mode="elements",include_page_break=True,infer_table_structure=False,languages=["Eng"],strategy="hi_res",include_metadata=True,model_name="chipper")`
However I keep getting the following error:
```
OSError: unstructuredio/chipper-v3 is not a local folder and is not a valid model identifier listed on 'https://huggingface.co./models'
If this is a private repository, make sure to pass a token having permission to this repo either by logging in with `huggingface-cli login` or by passing `token=<your_token>`
```
Not sure what Im missing here ?
Thanks
### 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
```
from langchain.document_loaders import UnstructuredPDFLoader
loader = UnstructuredPDFLoader(file_path="myfile.pdf", mode="elements",include_page_break=True,infer_table_structure=False,languages=["Eng"],strategy="hi_res",include_metadata=True,model_name="chipper")
documents = loader.load()
print(documents)
```
### Expected behavior
I should be getting the metadata similar to when I use other models like "yolox" which works fine. I heard chipper model is much better so I wanted to try it. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15299 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15299/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15299/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15299/events | https://github.com/langchain-ai/langchain/issues/15299 | 2,059,006,360 | I_kwDOIPDwls56ufGY | 15,299 | Cannot specify asyn clienct for OpenAIAssistantRunnable | {
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] | open | false | null | [] | null | 1 | 2023-12-29T02:29:20 | 2023-12-29T02:35:32 | null | CONTRIBUTOR | null | ### System Info
langchain = "^0.0.352"
@agola11
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Specify async open ai client upon intialization
client = openai.AsyncOpenAI()
assistant = OpenAIAssistantRunnable(assistant_id=self.assistant_id,as_agent=as_agent,client=client)
Produces error
pydantic.v1.errors.ConfigError: field "client" not yet prepared so type is still a ForwardRef, you might need to call OpenAIAssistantRunnable.update_forward_refs().
### Expected behavior
expect intialization to be successful | {
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- **Description:** integrate China Mobile Ecloud vector search,
- **Dependencies:** elasticsearch==7.10.1
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<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
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If you're adding a new integration, please include:
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Also, some agent toolkits (like pandas and python) will require more work | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15293 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15293/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15293/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15293/events | https://github.com/langchain-ai/langchain/pull/15293 | 2,058,946,805 | PR_kwDOIPDwls5i6X91 | 15,293 | Strip code block fences and extra test from xml when doing streaming … | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15292 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15292/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15292/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15292/events | https://github.com/langchain-ai/langchain/pull/15292 | 2,058,934,796 | PR_kwDOIPDwls5i6ViY | 15,292 | [docs] custom instructions | {
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LLM
ChatModels
Retrievers
Agents
Tools
I held off adding for vectorstore since its a bit more involved and we dont have a good base and i think its more likely people write custom retrievers than vectorstores? | {
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] | closed | false | null | [] | null | 3 | 2023-12-28T22:42:25 | 2023-12-30T12:46:52 | 2023-12-30T12:46:52 | NONE | null | ### System Info
Name: langchain
Version: 0.0.352
Name: openai
Version: 1.6.1
### Who can help?
_No response_
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
BASE_URL = "https://resource.openai.azure.com/"
API_KEY = "abc123"
DEPLOYMENT_NAME = "GPT35"
model = AzureChatOpenAI(
openai_api_base=BASE_URL,
openai_api_version="2023-05-15",
deployment_name=DEPLOYMENT_NAME,
openai_api_key=API_KEY,
openai_api_type="azure",
)
print(model(
[
HumanMessage(
content="Translate this sentence from English to French. I love programming."
)
]
))
### Expected behavior
I get an error about openai module. I get same error when i try and use embeddings.
I can use the openai azure pythons fine with my resource and api key - but langchain is broken.
AttributeError Traceback (most recent call last)
AttributeError: module 'openai' has no attribute 'error' | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15287 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15287/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15287/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15287/events | https://github.com/langchain-ai/langchain/pull/15287 | 2,058,906,800 | PR_kwDOIPDwls5i6PbQ | 15,287 | Make all json parsing less strict by default | {
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- Uses partial json recovery logic by default
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] | open | false | null | [] | null | 12 | 2023-12-28T21:26:16 | 2024-01-15T01:05:51 | null | NONE | null | ### System Info
langchain version: 0.0.348
Python 3.9.18
Mac OS M2 (Ventura 13.6.2)
AWS Bedrock Titan text express, Claude v2
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] 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
SQLDatabaseChain produces SQL query where the logic is correct but uses double quotes "identifier" hence incorrect for the snowflake SQL which require single quotes 'identifier'
output = SQL: SELECT "company" = "ABC"
desired output = SQL: SELECT 'company' = 'ABC'requires
### Expected behavior
desired out should be snowflake SQL single quotes for the identifier 'ABC' | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15284 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15284/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15284/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15284/events | https://github.com/langchain-ai/langchain/pull/15284 | 2,058,815,668 | PR_kwDOIPDwls5i564R | 15,284 | docs: cleanup rag use case | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15283 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15283/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15283/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15283/events | https://github.com/langchain-ai/langchain/issues/15283 | 2,058,773,284 | I_kwDOIPDwls56tmMk | 15,283 | AWS bedrock Claude v2 SQLDatabaseChain produces comments before the SQL Query | {
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] | open | false | null | [] | null | 19 | 2023-12-28T19:51:15 | 2024-01-15T02:49:42 | null | NONE | null | ### System Info
langchain Version: 0.0.348
python Version: Python 3.9.18
OS: Mac OS M2 (Ventura 13.6.2)
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
llm = Bedrock(
credentials_profile_name= os.environ.get('profile_name'),
model_id="anthropic.claude-v2",
model_kwargs={"temperature": 0.1},
endpoint_url="https://bedrock-runtime.us-east-1.amazonaws.com",
region_name="us-east-1",
verbose=True
)
db = SQLDatabase.from_uri(snowflake_url, sample_rows_in_table_info=3, include_tables=["table_name"])
output= SQLDatabaseChain.from_llm(
llm,
db,
prompt=few_shot_prompt,
return_intermediate_steps=True,
)
Gives the following error:
Error: syntax error line 1 at position 0 unexpected '**The**'.
[SQL: **The** query looks good to me, I don't see any of the common mistakes listed. Here is the original query again: SELECT *
FROM table]
### Expected behavior
the output should only produce SQL query outputted plainly, should not surround it in quotes or any comments prior to the SQL Query
Desired output:
[SQL: SELECT *
FROM table] | {
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- **Dependencies:** mistralai
- **Tag maintainer**: @efriis, @hwchase17
- **Twitter handle:** @LMS_David_RS
Create `integrations/text_embedding/mistralai.ipynb`: an example notebook for MistralAIEmbeddings class
Modify `embeddings/__init__.py`: Import the class
Create `embeddings/mistralai.py`: The embedding class
Create `integration_tests/embeddings/test_mistralai.py`: The test file.
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https://api.github.com/repos/langchain-ai/langchain/issues/15274 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15274/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15274/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15274/events | https://github.com/langchain-ai/langchain/pull/15274 | 2,058,651,209 | PR_kwDOIPDwls5i5YDg | 15,274 | revamp memory | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15271 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15271/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15271/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15271/events | https://github.com/langchain-ai/langchain/pull/15271 | 2,058,472,953 | PR_kwDOIPDwls5i4xni | 15,271 | community: llms.vertexai - Adds support for logprobs | {
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- **Description:** Adds support for logprobs within vertex ai, recently google started to support logprobs, this commit includes fast adaptation, Google recently started supporting logprobs, please check out this [page](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text#generative-ai-text-prompt-python_vertex_ai_sdk) for more details on their side.
- **Issue:** -,
- **Dependencies:** -,
- **Twitter handle:** [Burak Ömür](https://twitter.com/burkomr) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15270 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15270/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15270/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15270/events | https://github.com/langchain-ai/langchain/issues/15270 | 2,058,448,990 | I_kwDOIPDwls56sXBe | 15,270 | OpenAIAssistantRunnable stuck on execution with langchain tools | {
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] | closed | false | null | [] | null | 2 | 2023-12-28T13:33:35 | 2023-12-28T17:46:23 | 2023-12-28T17:46:23 | CONTRIBUTOR | null | ### System Info
```
from langchain.tools import DuckDuckGoSearchRun
from langchain.agents.openai_assistant import OpenAIAssistantRunnable
from langchain.agents import AgentExecutor
tools = [DuckDuckGoSearchRun()]
assistant = OpenAIAssistantRunnable.create_assistant(
name="langchain assistant",
instructions="You are a personal math tutor.",
tools=tools,
model="gpt-4-1106-preview",
as_agent=True,
)
logger.debug(assistant)
logger.debug(assistant.assistant_id)
agent_executor = AgentExecutor(agent=assistant, tools=tools,verbose=True)
response = agent_executor.invoke({"content": "whats the whether in london"})
print(response)
logger.debug(response)
```
I am trying to run the following from the example. It prints out the assitant information and id but after that it get completely stuck. I tried to step through the debugger but after while it continues and never comes back after calling
```
callback_manager = CallbackManager.configure(
callbacks,
self.callbacks,
self.verbose,
tags,
self.tags,
metadata,
self.metadata,
)
```
in the `__call__` method
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.tools import DuckDuckGoSearchRun
from langchain.agents.openai_assistant import OpenAIAssistantRunnable
from langchain.agents import AgentExecutor
tools = [DuckDuckGoSearchRun()]
assistant = OpenAIAssistantRunnable.create_assistant(
name="langchain assistant",
instructions="You are a personal math tutor.",
tools=tools,
model="gpt-4-1106-preview",
as_agent=True,
)
logger.debug(tai_assistant)
logger.debug(tai_assistant.assistant_id)
agent_executor = AgentExecutor(agent=assistant, tools=tools,verbose=True)
response = agent_executor.invoke({"content": "whats the whether in london"})
print(response)
logger.debug(response)
### Expected behavior
to have an output from the agent and not be stuck | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15269 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15269/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15269/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15269/events | https://github.com/langchain-ai/langchain/issues/15269 | 2,058,428,380 | I_kwDOIPDwls56sR_c | 15,269 | [mistralai]: Don´t support stop sequence | {
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] | closed | false | null | [] | null | 2 | 2023-12-28T13:14:32 | 2024-01-10T00:27:22 | 2024-01-10T00:27:22 | CONTRIBUTOR | null | ### System Info
Python: 3.11
Langchain: 0.0.352
mistralai: 0.0.8
### Who can help?
@efriis
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
If the ChatMistralAI model is used for an agent or similar, an error appears because the official Mistral API does not currently support the stop parameter (as other APIs such as OpenAI do).
### Expected behavior
Although this is something that should be fixed by Mistral in its official API, one of the following options should be done:
- Warn the user that this model cannot be used with a stop sequence before breaking execution due to the error.
- Implement an own solution for the stop sequence in the same package and do not send that parameter to the official client call. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15268 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15268/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15268/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15268/events | https://github.com/langchain-ai/langchain/pull/15268 | 2,058,421,290 | PR_kwDOIPDwls5i4mOP | 15,268 | langchain: Fix class name in RetryOutputParser docstring | {
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] | closed | false | null | [] | null | 1 | 2023-12-28T13:07:17 | 2023-12-28T16:03:46 | 2023-12-28T16:03:46 | CONTRIBUTOR | null | `OutputFixingParser` -> `RetryOutputParser`
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https://api.github.com/repos/langchain-ai/langchain/issues/15267 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15267/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15267/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15267/events | https://github.com/langchain-ai/langchain/pull/15267 | 2,058,390,197 | PR_kwDOIPDwls5i4fQy | 15,267 | community: Support openai_proxy compatible with openai>1.0.0 | {
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] | open | false | null | [] | null | 2 | 2023-12-28T12:39:16 | 2024-01-05T12:28:00 | null | CONTRIBUTOR | null | ### **Description:**
`openai` v1 no longer supports the method of setting `openai.proxy`. If using `OPENAI_PRPXY`, the users need to initialize an `http_client`.
### **Issue:**
https://github.com/langchain-ai/langchain/issues/13939 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15266 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15266/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15266/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15266/events | https://github.com/langchain-ai/langchain/issues/15266 | 2,058,376,378 | I_kwDOIPDwls56sFS6 | 15,266 | Manipulating database using chatgpt | {
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] | open | false | null | [] | null | 8 | 2023-12-28T12:24:15 | 2024-01-09T17:47:12 | null | NONE | null | ### System Info
Is there any way to manipulate the data in database like update, insert, delete through chatgpt chatbot with openai and langchain?
### 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
Is there any way to manipulate the data in database like update, insert, delete through chatgpt chatbot with openai and langchain?
### Expected behavior
possibility of manipulate the data in database like update, insert, delete through chatgpt chatbot with openai and langchain? | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15265 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15265/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15265/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15265/events | https://github.com/langchain-ai/langchain/pull/15265 | 2,058,340,862 | PR_kwDOIPDwls5i4UmL | 15,265 | community: Add ChatGLM3 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15264 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15264/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15264/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15264/events | https://github.com/langchain-ai/langchain/pull/15264 | 2,058,336,408 | PR_kwDOIPDwls5i4Tn6 | 15,264 | community: Make doctran synchronous | {
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] | closed | false | null | [] | null | 1 | 2023-12-28T11:41:31 | 2023-12-28T16:05:25 | 2023-12-28T16:05:25 | CONTRIBUTOR | null | ### Description
I found that the methods in [the doctran library](https://github.com/psychic-api/doctran) have been restructured into [synchronized versions](https://github.com/psychic-api/doctran/commit/14944a59f7513b51e629a850e0de5ae276eabed9),
And [the example ipynb](https://github.com/psychic-api/doctran/blob/main/examples.ipynb) also shows that the code is synchronized, but the README has not been updated yet.
so we need to modify the code and update the documentation.
### Issue
https://github.com/langchain-ai/langchain/issues/14645 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15262 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15262/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15262/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15262/events | https://github.com/langchain-ai/langchain/issues/15262 | 2,058,277,920 | I_kwDOIPDwls56rtQg | 15,262 | DOC: Issue with the page titled "Add Memory to OpenAI Functions Agent | 🦜️🔗 Langchain" | {
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] | closed | false | null | [] | null | 2 | 2023-12-28T10:39:12 | 2023-12-28T11:05:16 | 2023-12-28T11:04:44 | CONTRIBUTOR | null | ### Issue with current documentation:
According to the documentation listed under the page: https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions, adding a `BaseChatMemory` as `memory` property to an `OpenAIFunctionAgent` should add "memory" to the agent.
**Example listed under the page:**
>>Human: 'Hi'
>>Agent: 'How can I assist you today'
>>Human: 'My name is Bob'
>>Agent: 'Nice to meet you, Bob! How can I help you today?'
>>Human: 'What is my name'
>>Agent: 'Your name is Bob.'
**Actual result:**
>>Human: 'Hi'
>>Agent: 'How can I assist you today'
>>Human: 'My name is Bob'
>>Agent: 'Nice to meet you, Bob! How can I help you today?'
>>Human: 'What is my name'
>>Agent: 'I am not programmed to say your name'
RCA:
- The example implies the memory object that is passed to the functions agent instantiation actually takes care of converting the previous messages into required `ChatMessages` model, but implementation of such abstraction seems missing, atleast in langchain >= 0.0.350
- Upon checking with [visualizer](https://github.com/amosjyng/langchain-visualizer), it is seen that:
the latest invocation of agent does not include any "history" of any previous `run` with the `agent`. Curiously however, the agent executor does contain a variable `memory` which does enlist the previous conversations:
### Idea or request for content:
**Expected resolution:**
1. Update documentation to point to the correct way of incorporating memory with openai functions agent (ad-hoc implementation possibly)
2. Adding and updating implementation to make this API work as expected.
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https://api.github.com/repos/langchain-ai/langchain/issues/15261 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15261/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15261/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15261/events | https://github.com/langchain-ai/langchain/issues/15261 | 2,058,224,878 | I_kwDOIPDwls56rgTu | 15,261 | Get Chroma vectorstore Document by `doc_id` for document / metadata updates. | {
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] | open | false | null | [] | null | 1 | 2023-12-28T09:48:44 | 2023-12-28T09:57:29 | null | NONE | null | ### Feature request
It should be possible to search a Chroma vectorstore for a particular Document by it's ID. Given that the Document object is required for the `update_document` method, this lack of functionality makes it difficult to update document metadata, which should be a fairly common use-case.
Currently, there are two methods for searching a vectorstore, `get` and `search` but neither allow me to collect a Document by it's id
`vectorstore.get`: This allows for search via `id`, however, this does not return the actual `Document` object. Instead, the return is a dictionary of lists containing the `id`, `document`, and optionally, the `embeddings` for all matched documents. This provides an easy interface for utilising documents downstream, however, this creates a challenge for document updates as the `update_document` method needs the `Document` object to be passed, which would require needless recreation for updates.
`vectorstore.search`: This returns the `Document` object as required, however, it is not possible to explicitly search via `id`, only similarity search is possible.
As such, it appears that there is currently no easy way to do this at present, without manually recreating the Document from the `get` output.
### Motivation
For my use-case, I am performing offline clustering of my embeddings in order to find the core groups of documents and would like to add the predicted label to each document as metadata "cluster_label".
Below is a simple representation of my current pipeline:
```
all_docs = vectorstore.get(include=["embeddings", "documents"])
doc_ids = all_docs["ids"]
embeddings = np.array(all_docs["embeddings"])
cluster_model, labels = fit_predict_clustering(embeddings, max_components=10)
for doc_id, label in zip(ids, labels):
# Fetch the document from the vectorstore
doc = vectorstore.get(doc_id) # returns Dict[str, List], but I need Document
# Given current implementation, I would need to now convert the above dictionary to Document
...
# Update metadata with the cluster label
doc.metadata["cluster_label"] = label
vectorstore.update(doc_id, doc)
```
### Your contribution
I'm happy to contribute to this feature if deemed beneficial. To my mind, it should be achievable by either:
1. Updating the get method to allow `Document` returning,
2. Including a new method with the required functionality, or
3. Providing a utility for easy bulk conversion from `get` output to `List[Document]`.
However, I'm open to suggestions as to the most fitting solution.
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https://api.github.com/repos/langchain-ai/langchain/issues/15260 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15260/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15260/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15260/events | https://github.com/langchain-ai/langchain/pull/15260 | 2,058,209,122 | PR_kwDOIPDwls5i33y5 | 15,260 | Documentation: Update playwright documentation for langchain version >= 0.0.351 | {
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] | closed | false | null | [] | null | 1 | 2023-12-28T09:34:14 | 2024-01-01T21:38:57 | 2024-01-01T21:38:57 | CONTRIBUTOR | null | - A documentation change in the example listed under: https://python.langchain.com/docs/integrations/toolkits/playwright
- `create_async_playwright_browser` does not exist under the module: `langchain.tools.playwright.utils` post >= 0.0.351 version
- No dependencies to be changed
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] | null | 5 | 2023-12-28T08:59:39 | 2024-01-17T10:40:37 | null | CONTRIBUTOR | null | copy from #7920 which closed and no updated. and fix https://github.com/langchain-ai/langchain/issues/15253 .
If this has any issues, I'll fix the code and update.
If this offends anyone I will close it immediately. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15256 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15256/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15256/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15256/events | https://github.com/langchain-ai/langchain/issues/15256 | 2,058,104,532 | I_kwDOIPDwls56rC7U | 15,256 | qdrant.amax_marginal_relevance_search have not results but qdrant.max_marginal_relevance_search hava results | {
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] | closed | false | null | [] | null | 1 | 2023-12-28T07:41:26 | 2023-12-29T03:31:51 | 2023-12-29T03:31:51 | NONE | null | ### System Info
0.0.350
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
help(qdrant.amax_marginal_relevance_search)
print("&&&&&&&&&&&&&&&&&")
help(qdrant.max_marginal_relevance_search)
hits = await qdrant.amax_marginal_relevance_search(text, k=20, fetch_k=100,filter=filter_empty)
print(hits)
hits1 = qdrant.max_marginal_relevance_search(text, k=20, fetch_k=100,filter=filter_empty)
print(hits1)
### Expected behavior
qdrant.amax_marginal_relevance_search have not results but qdrant.max_marginal_relevance_search hava results | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15255 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15255/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15255/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15255/events | https://github.com/langchain-ai/langchain/issues/15255 | 2,058,083,922 | I_kwDOIPDwls56q95S | 15,255 | langchain 0.5.7 not match latest openai | {
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] | open | false | null | [] | null | 1 | 2023-12-28T07:17:09 | 2023-12-28T07:28:50 | null | NONE | null | ### System Info
Python: 3.10
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo",
temperature=0.8,
max_tokens=60)
error occurs at openai.py, error message is: AttributeError: module 'openai' has no attribute 'OpenAI'
the reason, I guess, is version not match.
### 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
import os
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
HumanMessage,
SystemMessage
)
openai = ChatOpenAI(model_name="gpt-3.5-turbo",
temperature=0.8,
max_tokens=60)
messages = [
SystemMessage(content="bla"),
HumanMessage(content="bla")
]
response = openai(messages)
print(response)
### Expected behavior
no exception | {
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] | open | false | null | [] | null | 1 | 2023-12-28T06:29:24 | 2023-12-28T06:31:37 | null | NONE | null | ### Feature request
Similar to the way callbacks are implemented in BaseLLM the embedding class should also support callbacks.
### Motivation
When using embedding models in a RAG application it would be useful to track e.g. the number of tokens.
Callbacks can be used to log usage details to monitoring services (eg Langsmith).
### Your contribution
There is a closed PR adressing the same topic https://github.com/langchain-ai/langchain/pull/7920 | {
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] | closed | false | null | [] | null | 1 | 2023-12-28T06:08:46 | 2023-12-28T06:12:33 | 2023-12-28T06:12:33 | COLLABORATOR | null |
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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,
- **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` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
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/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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- **Issue:** This fixes [Issue #14631](https://github.com/langchain-ai/langchain/issues/14631)
- **Twitter handle:** [@consultchrisq ](https://twitter.com/consultchrisq?lang=en)
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- **Issue:** This fixes [Issue #14631](https://github.com/langchain-ai/langchain/issues/14631)
- **Twitter handle:** [@consultchrisq ](https://twitter.com/consultchrisq?lang=en)
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What should I do if I want to log the number of tokens shot with llm in chain via lcel?
### Suggestion:
lcel chain token usage tracking | {
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- **Issue:** https://github.com/langchain-ai/langchain/issues/14550#issuecomment-1867445049
- **Dependencies:** None,
- **Twitter handle:** None | {
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I do not understand how chains are built with the transfer of information between generations. here is an example of the code in the langchain [documentation](https://python.langchain.com/docs/expression_language/why):
```
from langchain_core.runnables import RunnablePassthrough
prompt = ChatPromptTemplate.from_template(
"Tell me a short joke about {topic}"
)
output_parser = StrOutputParser()
model = llm
chain = (
{"topic": RunnablePassthrough()}
| prompt
| model
| output_parser
)
chain.invoke("ice cream")
```
here in the promo, please write a joke about ice cream, based on this example, my question will be: "how to make the chain continue further and, for example, analyze this joke (that is, work further with what was generated)."
There was an idea to just create a second promt and add it to the chain:
```
prompt = ChatPromptTemplate.from_template(
"Tell me a short joke about {topic}"
)
prompt1 = ChatPromptTemplate.from_template(
"What was the joke about?"
)
output_parser = StrOutputParser()
model = llm
chain = (
{"topic": RunnablePassthrough()}
| prompt
| model
| output_parser
| prompt1
| model
| output_parser
)
```
But it won't work that way, because for some reason the model doesn't know the context...
### Idea or request for content:
_No response_ | {
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Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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,
- **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` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
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,
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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] | closed | false | null | [] | null | 1 | 2023-12-28T02:53:57 | 2023-12-28T06:06:43 | 2023-12-28T06:06:43 | CONTRIBUTOR | null | ### Issue you'd like to raise.
Based on the documentation and RFC standards referenced in the links:
- https://peps.python.org/pep-0604/
- https://www.blog.pythonlibrary.org/2021/09/11/python-3-10-simplifies-unions-in-type-annotations/
it's evident that the introduction of using | instead of 'union' for type annotations is a feature that was introduced in Python 3.10.
However, I've observed that in our project's pyproject.toml and ci.yaml files, the Python version is specified as python = ">=3.8.1,<4.0".
This leads me to question whether LangChain will face issues with type checking or even running in the specified Python 3.8 environment, given that it doesn't support the | syntax for unions.
If there are any considerations or plans, such as updating the pyproject.toml and ci.yaml to make LangChain compatible with a minimum of Python 3.10, or if it's appropriate for me to submit a PR to address the use of the | operator in type annotations within LangChain, I'd appreciate your input and guidance.
### Suggestion:
Upgrade the python version, or fix and remove | syntax, I would be happy to do this, please let me know your decision
@hwchase17 | {
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] | open | false | null | [] | null | 3 | 2023-12-28T02:17:17 | 2023-12-28T02:38:55 | null | NONE | null | ### System Info
如何对langchain加载的chatglm-6b模型进行量化处理
### Who can help?
@hwchase17 @hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# 本地模型
else:
from configs.model_config import VLLM_MODEL_DICT
if kwargs["model_names"][0] in VLLM_MODEL_DICT and args.infer_turbo == "vllm":
import fastchat.serve.vllm_worker
from fastchat.serve.vllm_worker import VLLMWorker, app, worker_id
from vllm import AsyncLLMEngine
from vllm.engine.arg_utils import AsyncEngineArgs,EngineArgs
args.tokenizer = args.model_path # 如果tokenizer与model_path不一致在此处添加
args.tokenizer_mode = 'auto'
args.trust_remote_code= True
args.download_dir= None
args.load_format = 'auto'
args.dtype = 'auto'
args.seed = 0
args.worker_use_ray = False
args.pipeline_parallel_size = 1
args.tensor_parallel_size = 1
args.block_size = 16
args.swap_space = 4 # GiB
args.gpu_memory_utilization = 0.90
args.max_num_batched_tokens = None # 一个批次中的最大令牌（tokens）数量，这个取决于你的显卡和大模型设置，设置太大显存会不够
args.max_num_seqs = 256
args.disable_log_stats = False
args.conv_template = None
args.limit_worker_concurrency = 5
args.no_register = False
args.num_gpus = 4 # vllm worker的切分是tensor并行，这里填写显卡的数量
args.engine_use_ray = False
args.disable_log_requests = False
# 0.2.1 vllm后要加的参数, 但是这里不需要
args.max_model_len = None
args.revision = None
args.quantization = None
args.max_log_len = None
args.tokenizer_revision = None
# 0.2.2 vllm需要新加的参数
args.max_paddings = 256
if args.model_path:
args.model = args.model_path
if args.num_gpus > 1:
args.tensor_parallel_size = args.num_gpus
for k, v in kwargs.items():
setattr(args, k, v)
engine_args = AsyncEngineArgs.from_cli_args(args)
engine = AsyncLLMEngine.from_engine_args(engine_args)
worker = VLLMWorker(
controller_addr = args.controller_address,
worker_addr = args.worker_address,
worker_id = worker_id,
model_path = args.model_path,
model_names = args.model_names,
limit_worker_concurrency = args.limit_worker_concurrency,
no_register = args.no_register,
llm_engine = engine,
conv_template = args.conv_template,
)
sys.modules["fastchat.serve.vllm_worker"].engine = engine
sys.modules["fastchat.serve.vllm_worker"].worker = worker
sys.modules["fastchat.serve.vllm_worker"].logger.setLevel(log_level)
### Expected behavior
这里加载本地模型的时候如何对模型进行量化 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15242 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15242/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15242/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15242/events | https://github.com/langchain-ai/langchain/pull/15242 | 2,057,897,920 | PR_kwDOIPDwls5i21hT | 15,242 | community: allow additional kwargs in MlflowEmbeddings for compatibility with Cohere API | {
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] | open | false | null | [] | null | 3 | 2023-12-28T01:46:15 | 2024-01-11T03:47:56 | null | NONE | null | <!-- Thank you for contributing to LangChain!
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** add support for kwargs in`MlflowEmbeddings` `embed_document()` and `embed_query()` so that all the arguments required by Cohere API (and others?) can be passed down to the server.
- **Issue:** #15234
- **Dependencies:** MLflow with MLflow Deployments (`pip install mlflow[genai]`)
**Tests**
Now this code [adapted from the docs](https://python.langchain.com/docs/integrations/providers/mlflow#embeddings-example) for the Cohere API works locally.
```python
"""
Setup
-----
export COHERE_API_KEY=...
mlflow deployments start-server --config-path examples/deployments/cohere/config.yaml
Run
---
python /path/to/this/file.py
"""
embeddings = MlflowCohereEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings")
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
```
Output
```
[0.060455322, 0.028793335, -0.025848389]
[0.031707764, 0.021057129, -0.009361267]
``` | {
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<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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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/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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] | closed | false | null | [] | null | 1 | 2023-12-28T01:10:59 | 2023-12-28T01:24:35 | 2023-12-28T01:24:35 | NONE | null | ### System Info
I used the standard code example from the langchain documentation about Fireworks where I inserted my API key. That's the mistake I made:
```
[llm/start] [1:llm:Fireworks] Entering LLM run with input:
{
"prompts": [
"Name 3 sports."
]
}
[llm/error] [1:llm:Fireworks] [761ms] LLM run errored with error:
"AuthenticationError({'fault': {'faultstring': 'Invalid ApiKey', 'detail': {'errorcode': 'oauth.v2.InvalidApiKey'}}})Traceback (most recent call last):\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\langchain_core\\language_models\\llms.py\", line 540, in _generate_helper\n self._generate(\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\langchain_community\\llms\\fireworks.py\", line 100, in _generate\n response = completion_with_retry_batching(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\langchain_community\\llms\\fireworks.py\", line 296, in completion_with_retry_batching\n return batch_sync_run()\n ^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\langchain_community\\llms\\fireworks.py\", line 293, in batch_sync_run\n results = list(executor.map(_completion_with_retry, prompt))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Program Files\\Python311\\Lib\\concurrent\\futures\\_base.py\", line 619, in result_iterator\n yield _result_or_cancel(fs.pop())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Program Files\\Python311\\Lib\\concurrent\\futures\\_base.py\", line 317, in _result_or_cancel\n return fut.result(timeout)\n ^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Program Files\\Python311\\Lib\\concurrent\\futures\\_base.py\", line 456, in result\n return self.__get_result()\n ^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Program Files\\Python311\\Lib\\concurrent\\futures\\_base.py\", line 401, in __get_result\n raise self._exception\n\n\n File \"C:\\Program Files\\Python311\\Lib\\concurrent\\futures\\thread.py\", line 58, in run\n result = self.fn(*self.args, **self.kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\tenacity\\__init__.py\", line 289, in wrapped_f\n return self(f, *args, **kw)\n ^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\tenacity\\__init__.py\", line 379, in __call__\n do = self.iter(retry_state=retry_state)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\tenacity\\__init__.py\", line 314, in iter\n return fut.result()\n ^^^^^^^^^^^^\n\n\n File \"C:\\Program Files\\Python311\\Lib\\concurrent\\futures\\_base.py\", line 449, in result\n return self.__get_result()\n ^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Program Files\\Python311\\Lib\\concurrent\\futures\\_base.py\", line 401, in __get_result\n raise self._exception\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\tenacity\\__init__.py\", line 382, in __call__\n result = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\langchain_community\\llms\\fireworks.py\", line 289, in _completion_with_retry\n return fireworks.client.Completion.create(**kwargs, prompt=prompt)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\fireworks\\client\\base_completion.py\", line 80, in create\n return cls._create_non_streaming(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\fireworks\\client\\base_completion.py\", line 158, in _create_non_streaming\n response = client.post_request_non_streaming(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\fireworks\\client\\api_client.py\", line 125, in post_request_non_streaming\n self._error_handling(response)\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\fireworks\\client\\api_client.py\", line 91, in _error_handling\n self._raise_for_status(resp)\n\n\n File \"C:\\Users\\akidra\\AppData\\Roaming\\Python\\Python311\\site-packages\\fireworks\\client\\api_client.py\", line 67, in _raise_for_status\n raise AuthenticationError(resp.json())\n\n\nfireworks.client.error.AuthenticationError: {'fault': {'faultstring': 'Invalid ApiKey', 'detail': {'errorcode': 'oauth.v2.InvalidApiKey'}}}"
---------------------------------------------------------------------------
AuthenticationError Traceback (most recent call last)
Cell In[25], line 7
1 from langchain.llms.fireworks import Fireworks
3 llm = Fireworks(
4 fireworks_api_key="<BPR7ILI5ar0xAVWKwwAPvE8cyL2yBFpJRGqDGU3QirD6N8W0>",
5 model="accounts/fireworks/models/mixtral-8x7b-instruct",
6 max_tokens=256)
----> 7 llm("Name 3 sports.")
File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\language_models\llms.py:892, in BaseLLM.__call__(self, prompt, stop, callbacks, tags, metadata, **kwargs)
885 if not isinstance(prompt, str):
886 raise ValueError(
887 "Argument `prompt` is expected to be a string. Instead found "
888 f"{type(prompt)}. If you want to run the LLM on multiple prompts, use "
889 "`generate` instead."
890 )
891 return (
--> 892 self.generate(
893 [prompt],
894 stop=stop,
895 callbacks=callbacks,
896 tags=tags,
897 metadata=metadata,
898 **kwargs,
899 )
900 .generations[0][0]
901 .text
902 )
File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\language_models\llms.py:666, in BaseLLM.generate(self, prompts, stop, callbacks, tags, metadata, run_name, **kwargs)
650 raise ValueError(
651 "Asked to cache, but no cache found at `langchain.cache`."
652 )
653 run_managers = [
654 callback_manager.on_llm_start(
655 dumpd(self),
(...)
664 )
665 ]
--> 666 output = self._generate_helper(
667 prompts, stop, run_managers, bool(new_arg_supported), **kwargs
668 )
669 return output
670 if len(missing_prompts) > 0:
File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\language_models\llms.py:553, in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
551 for run_manager in run_managers:
552 run_manager.on_llm_error(e, response=LLMResult(generations=[]))
--> 553 raise e
554 flattened_outputs = output.flatten()
555 for manager, flattened_output in zip(run_managers, flattened_outputs):
File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\language_models\llms.py:540, in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
530 def _generate_helper(
531 self,
532 prompts: List[str],
(...)
536 **kwargs: Any,
537 ) -> LLMResult:
538 try:
539 output = (
--> 540 self._generate(
541 prompts,
542 stop=stop,
543 # TODO: support multiple run managers
544 run_manager=run_managers[0] if run_managers else None,
545 **kwargs,
546 )
547 if new_arg_supported
548 else self._generate(prompts, stop=stop)
549 )
550 except BaseException as e:
551 for run_manager in run_managers:
File ~\AppData\Roaming\Python\Python311\site-packages\langchain_community\llms\fireworks.py:100, in Fireworks._generate(self, prompts, stop, run_manager, **kwargs)
98 choices = []
99 for _prompts in sub_prompts:
--> 100 response = completion_with_retry_batching(
101 self,
102 self.use_retry,
103 prompt=_prompts,
104 run_manager=run_manager,
105 stop=stop,
106 **params,
107 )
108 choices.extend(response)
110 return self.create_llm_result(choices, prompts)
File ~\AppData\Roaming\Python\Python311\site-packages\langchain_community\llms\fireworks.py:296, in completion_with_retry_batching(llm, use_retry, run_manager, **kwargs)
293 results = list(executor.map(_completion_with_retry, prompt))
294 return results
--> 296 return batch_sync_run()
File ~\AppData\Roaming\Python\Python311\site-packages\langchain_community\llms\fireworks.py:293, in completion_with_retry_batching.<locals>.batch_sync_run()
291 def batch_sync_run() -> List:
292 with ThreadPoolExecutor() as executor:
--> 293 results = list(executor.map(_completion_with_retry, prompt))
294 return results
File C:\Program Files\Python311\Lib\concurrent\futures\_base.py:619, in Executor.map.<locals>.result_iterator()
616 while fs:
617 # Careful not to keep a reference to the popped future
618 if timeout is None:
--> 619 yield _result_or_cancel(fs.pop())
620 else:
621 yield _result_or_cancel(fs.pop(), end_time - time.monotonic())
File C:\Program Files\Python311\Lib\concurrent\futures\_base.py:317, in _result_or_cancel(***failed resolving arguments***)
315 try:
316 try:
--> 317 return fut.result(timeout)
318 finally:
319 fut.cancel()
File C:\Program Files\Python311\Lib\concurrent\futures\_base.py:456, in Future.result(self, timeout)
454 raise CancelledError()
455 elif self._state == FINISHED:
--> 456 return self.__get_result()
457 else:
458 raise TimeoutError()
File C:\Program Files\Python311\Lib\concurrent\futures\_base.py:401, in Future.__get_result(self)
399 if self._exception:
400 try:
--> 401 raise self._exception
402 finally:
403 # Break a reference cycle with the exception in self._exception
404 self = None
File C:\Program Files\Python311\Lib\concurrent\futures\thread.py:58, in _WorkItem.run(self)
55 return
57 try:
---> 58 result = self.fn(*self.args, **self.kwargs)
59 except BaseException as exc:
60 self.future.set_exception(exc)
File ~\AppData\Roaming\Python\Python311\site-packages\tenacity\__init__.py:289, in BaseRetrying.wraps.<locals>.wrapped_f(*args, **kw)
287 @functools.wraps(f)
288 def wrapped_f(*args: t.Any, **kw: t.Any) -> t.Any:
--> 289 return self(f, *args, **kw)
File ~\AppData\Roaming\Python\Python311\site-packages\tenacity\__init__.py:379, in Retrying.__call__(self, fn, *args, **kwargs)
377 retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
378 while True:
--> 379 do = self.iter(retry_state=retry_state)
380 if isinstance(do, DoAttempt):
381 try:
File ~\AppData\Roaming\Python\Python311\site-packages\tenacity\__init__.py:314, in BaseRetrying.iter(self, retry_state)
312 is_explicit_retry = fut.failed and isinstance(fut.exception(), TryAgain)
313 if not (is_explicit_retry or self.retry(retry_state)):
--> 314 return fut.result()
316 if self.after is not None:
317 self.after(retry_state)
File C:\Program Files\Python311\Lib\concurrent\futures\_base.py:449, in Future.result(self, timeout)
447 raise CancelledError()
448 elif self._state == FINISHED:
--> 449 return self.__get_result()
451 self._condition.wait(timeout)
453 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
File C:\Program Files\Python311\Lib\concurrent\futures\_base.py:401, in Future.__get_result(self)
399 if self._exception:
400 try:
--> 401 raise self._exception
402 finally:
403 # Break a reference cycle with the exception in self._exception
404 self = None
File ~\AppData\Roaming\Python\Python311\site-packages\tenacity\__init__.py:382, in Retrying.__call__(self, fn, *args, **kwargs)
380 if isinstance(do, DoAttempt):
381 try:
--> 382 result = fn(*args, **kwargs)
383 except BaseException: # noqa: B902
384 retry_state.set_exception(sys.exc_info()) # type: ignore[arg-type]
File ~\AppData\Roaming\Python\Python311\site-packages\langchain_community\llms\fireworks.py:289, in completion_with_retry_batching.<locals>._completion_with_retry(prompt)
287 @conditional_decorator(use_retry, retry_decorator)
288 def _completion_with_retry(prompt: str) -> Any:
--> 289 return fireworks.client.Completion.create(**kwargs, prompt=prompt)
File ~\AppData\Roaming\Python\Python311\site-packages\fireworks\client\base_completion.py:80, in BaseCompletion.create(cls, model, prompt_or_messages, request_timeout, stream, client, **kwargs)
76 return cls._create_streaming(
77 model, request_timeout, client=client, **kwargs
78 )
79 else:
---> 80 return cls._create_non_streaming(
81 model, request_timeout, client=client, **kwargs
82 )
File ~\AppData\Roaming\Python\Python311\site-packages\fireworks\client\base_completion.py:158, in BaseCompletion._create_non_streaming(cls, model, request_timeout, client, **kwargs)
156 client = client or FireworksClient(request_timeout=request_timeout)
157 data = {"model": model, "stream": False, **kwargs}
--> 158 response = client.post_request_non_streaming(
159 f"{client.base_url}/{cls.endpoint}", data=data
160 )
161 return cls.response_class(**response)
File ~\AppData\Roaming\Python\Python311\site-packages\fireworks\client\api_client.py:125, in FireworksClient.post_request_non_streaming(self, url, data)
119 with httpx.Client(
120 headers={"Authorization": f"Bearer {self.api_key}"},
121 timeout=self.request_timeout,
122 **self.client_kwargs,
123 ) as client:
124 response = client.post(url, json=data)
--> 125 self._error_handling(response)
126 return response.json()
File ~\AppData\Roaming\Python\Python311\site-packages\fireworks\client\api_client.py:91, in FireworksClient._error_handling(self, resp)
89 if resp.is_error:
90 resp.read()
---> 91 self._raise_for_status(resp)
File ~\AppData\Roaming\Python\Python311\site-packages\fireworks\client\api_client.py:67, in FireworksClient._raise_for_status(self, resp)
65 raise InvalidRequestError(resp.json())
66 elif resp.status_code == 401:
---> 67 raise AuthenticationError(resp.json())
68 elif resp.status_code == 403:
69 raise PermissionError(resp.json())
AuthenticationError: {'fault': {'faultstring': 'Invalid ApiKey', 'detail': {'errorcode': 'oauth.v2.InvalidApiKey'}}}
```
### Who can help?
@eyurtsev
### 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
1. i use https://python.langchain.com/docs/integrations/providers/fireworks
2. got the API key in https://app.fireworks.ai/api-keys
3. I inserted my key into this code:
```
from langchain.llms.fireworks import Fireworks
import os
os.environ["FIREWORKS_API_KEY"] = "<My key was here.>"
llm = Fireworks(fireworks_api_key="<My key was here.>")
llm = Fireworks(
fireworks_api_key="<My key was here.>",
model="accounts/fireworks/models/mixtral-8x7b-instruct",
max_tokens=256)
llm("Name 3 sports.")
```
### Expected behavior
this example is from the documentation - I just want it to work to move on. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15238 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15238/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15238/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15238/events | https://github.com/langchain-ai/langchain/pull/15238 | 2,057,879,588 | PR_kwDOIPDwls5i2xyW | 15,238 | Retrieval Docs Revamp | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15237 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15237/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15237/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15237/events | https://github.com/langchain-ai/langchain/issues/15237 | 2,057,877,277 | I_kwDOIPDwls56qLcd | 15,237 | DOC: langchain plus OpenAI-compatible URL API equally error | {
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] | closed | false | null | [] | null | 6 | 2023-12-28T00:56:43 | 2024-01-04T16:19:09 | 2024-01-04T16:19:09 | NONE | null | ### Issue with current documentation:
hello everyone! Is it possible to use the OpenAI-compatible URL API from text-generation-webui with langchain? the langchain [documentation](https://python.langchain.com/docs/integrations/llms/textgen) says about localhost, but I don't have access to it, I tried to insert the link into model_url, the error appeared both in google colab and in the terminal.
### Idea or request for content:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15236 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15236/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15236/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15236/events | https://github.com/langchain-ai/langchain/issues/15236 | 2,057,867,182 | I_kwDOIPDwls56qI-u | 15,236 | Issue: validation errors for ConversationalRetrievalChain | {
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] | open | false | null | [] | null | 3 | 2023-12-28T00:30:51 | 2023-12-28T00:50:03 | null | NONE | null | ### Issue you'd like to raise.
I am receiving this error 2 validation errors for ConversationalRetrievalChain
qa_template
extra fields not permitted (type=value_error.extra)
question_generator_chain_options
extra fields not permitted (type=value_error.extra) , for the following code :
```
retriever = vector_store.as_retriever()
sales_persona_prompt = PromptTemplate.from_template(SALES_PERSONA_PROMPT)
condense_prompt = PromptTemplate.from_template(CONDENSE_PROMPT)
question_generator_chain_options = {
"llm": non_streaming_model,
"template": condense_prompt,
}
chain = ConversationalRetrievalChain.from_llm(
streaming_model,
retriever,
qa_template=sales_persona_prompt,
question_generator_chain_options=question_generator_chain_options,
return_source_documents=False,
)
```
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15235 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15235/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15235/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15235/events | https://github.com/langchain-ai/langchain/issues/15235 | 2,057,862,832 | I_kwDOIPDwls56qH6w | 15,235 | Executing Chain with HuggingFace Models using wrapper | {
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] | open | false | null | [] | null | 1 | 2023-12-28T00:20:19 | 2023-12-28T00:27:48 | null | NONE | null | ### System Info
Langchain version: 0.0.340
Python version: 3.11.0
### Who can help?
@hwchase17
@agola11
### 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 am trying to use the HuggingFace Hub Wrapper to create a chat model instance and use the model in a chain. However these seems to be some library discrepancies between various base files.
Below is the code that works:
from langchain_community.llms import HuggingFaceHub
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_community.chat_models.huggingface import ChatHuggingFace
from langchain.prompts import PromptTemplate, ChatPromptTemplate
llm = HuggingFaceHub(
repo_id="HuggingFaceH4/zephyr-7b-beta",
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 30,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
chat_model = ChatHuggingFace(llm=llm)
messages = [
SystemMessage(content="You're a zoologist who is able to answer questions about various animals. You are tasked with answering the following question provided"),
HumanMessage(content="What is the average lifespan of an Elephant?"),
]
res = chat_model.invoke(messages)
print(res.content)
I want to modify this to allow the prompt to be more dynamic and potentially include a chain of prompts. Here is my modification:
prompt = ChatPromptTemplate.from_messages(
[
SystemMessage(content="You're a zoologist who is able to answer questions about various animals. You are tasked with answering the following question provided"),
HumanMessage(content="What is the average lifespan of an {animal}?"),
]
)
chain1 = prompt| chat_model
chain1.invoke({"animal": "giraffe"})
I get the following error: NotImplementedError: Unsupported message type: <class 'langchain_core.messages.system.SystemMessage'> - this is because in the chat.py file the import statement for the messages is the following: from langchain.schema.messages import (
AIMessage,
AnyMessage,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
get_buffer_string,
). However the updated version I found in documentation states to use langchain_core.messages.
Even if I update the import statement to be the old version, I run into the following error: TypeError: 'ChatPromptValue' object is not subscriptable.
### Expected behavior
I should be able to execute the chain and receive the same output from the non-dynamic verison of the code - res.content output. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15234 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15234/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15234/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15234/events | https://github.com/langchain-ai/langchain/issues/15234 | 2,057,854,254 | I_kwDOIPDwls56qF0u | 15,234 | MlflowEmbeddings: input_type argument is missing, required by Cohere embeddings models | {
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] | open | false | null | [] | null | 2 | 2023-12-27T23:59:40 | 2023-12-28T23:42:11 | null | NONE | null | ### System Info
python = "3.11"
langchain = "0.0.352"
cohere = "4.39"
mlflow = {extras = ["genai"], version = "2.9.2"}
### Who can help?
@harupy
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I followed the official example for embeddings here, except that I am using Cohere instead of OpenAI: https://python.langchain.com/docs/integrations/providers/mlflow
<details>
<summary>Click for specific steps</summary>
More specifically, I installed mlflow genai and set my `COHERE_API_KEY` environment variable:
```bash
pip install 'mlflow[genai]'
export COHERE_API_KEY=...
```
I created `config.yaml` like so:
```yaml
endpoints:
- name: completions
endpoint_type: llm/v1/completions
model:
provider: cohere
name: command
config:
cohere_api_key: $COHERE_API_KEY
- name: embeddings
endpoint_type: llm/v1/embeddings
model:
provider: cohere
name: embed-english-light-v3.0
config:
cohere_api_key: $COHERE_API_KEY
```
I started the mlflow deployments server:
```bash
mlflow deployments start-server --config-path config.yaml
```
<details>
<summary>The server started as expected</summary>
```
xxx/python3.11/site-packages/pydantic/_internal/_config.py:321: UserWarning: Valid config keys have changed in V2:
* 'schema_extra' has been renamed to 'json_schema_extra'
warnings.warn(message, UserWarning)
[2023-12-27 13:53:18 -0800] [22480] [INFO] Starting gunicorn 21.2.0
[2023-12-27 13:53:18 -0800] [22480] [INFO] Listening at: http://127.0.0.1:5000 (22480)
[2023-12-27 13:53:18 -0800] [22480] [INFO] Using worker: uvicorn.workers.UvicornWorker
[2023-12-27 13:53:18 -0800] [22481] [INFO] Booting worker with pid: 22481
[2023-12-27 13:53:18 -0800] [22482] [INFO] Booting worker with pid: 22482
xxx/python3.11/site-packages/pydantic/_internal/_config.py:321: UserWarning: Valid config keys have changed in V2:
* 'schema_extra' has been renamed to 'json_schema_extra'
warnings.warn(message, UserWarning)
xxx/python3.11/site-packages/pydantic/_internal/_config.py:321: UserWarning: Valid config keys have changed in V2:
* 'schema_extra' has been renamed to 'json_schema_extra'
warnings.warn(message, UserWarning)
[2023-12-27 13:53:20 -0800] [22481] [INFO] Started server process [22481]
[2023-12-27 13:53:20 -0800] [22481] [INFO] Waiting for application startup.
[2023-12-27 13:53:20 -0800] [22481] [INFO] Application startup complete.
[2023-12-27 13:53:20 -0800] [22482] [INFO] Started server process [22482]
[2023-12-27 13:53:20 -0800] [22482] [INFO] Waiting for application startup.
[2023-12-27 13:53:20 -0800] [22482] [INFO] Application startup complete.
```
</details>
In `test.py`, I added the embeddings example:
```python
from langchain.embeddings import MlflowEmbeddings
embeddings = MlflowEmbeddings(
target_uri="http://127.0.0.1:5000",
endpoint="embeddings",
)
print(embeddings.embed_query("hello"))
print(embeddings.embed_documents(["hello"]))
```
And I ran it with `python test.py`.
</details>
Here is the error I got:
```
raise HTTPError(
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://127.0.0.1:5000/endpoints/embeddings/invocations. Response text: {"detail":{"message":"invalid request: valid input_type must be provided with the provided model"}}
```
<details>
<summary>Full trace</summary>
```
xxx/python3.11/site-packages/pydantic/_internal/_config.py:321: UserWarning: Valid config keys have changed in V2:
* 'schema_extra' has been renamed to 'json_schema_extra'
warnings.warn(message, UserWarning)
Traceback (most recent call last):
File "xxx/python3.11/site-packages/mlflow/utils/request_utils.py", line 52, in augmented_raise_for_status
response.raise_for_status()
File "xxx/python3.11/site-packages/requests/models.py", line 1021, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://127.0.0.1:5000/endpoints/embeddings/invocations
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "yyy/test.py", line 8, in <module>
print(embeddings.embed_query("hello"))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "xxx/python3.11/site-packages/langchain_community/embeddings/mlflow.py", line 74, in embed_query
return self.embed_documents([text])[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "xxx/python3.11/site-packages/langchain_community/embeddings/mlflow.py", line 69, in embed_documents
resp = self._client.predict(endpoint=self.endpoint, inputs={"input": txt})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "xxx/python3.11/site-packages/mlflow/deployments/mlflow/__init__.py", line 294, in predict
return self._call_endpoint(
^^^^^^^^^^^^^^^^^^^^
File "xxx/python3.11/site-packages/mlflow/deployments/mlflow/__init__.py", line 139, in _call_endpoint
augmented_raise_for_status(response)
File "xxx/python3.11/site-packages/mlflow/utils/request_utils.py", line 55, in augmented_raise_for_status
raise HTTPError(
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://127.0.0.1:5000/endpoints/embeddings/invocations. Response text: {"detail":{"message":"invalid request: valid input_type must be provided with the provided model"}}
```
</details>
The issue is that `embed_query`/`embed_documents` don't allow passing in the input_type argument, which is needed by the Cohere API -- see https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/embeddings/cohere.py#L81
### Proposed solution
My quick solution was to modify the two methods in `MlflowEmbeddings` to allow for kwargs:
```python
class MlflowEmbeddings:
....
def embed_documents(self, texts: List[str], **kwargs) -> List[List[float]]:
embeddings: List[List[float]] = []
for txt in _chunk(texts, 20):
resp = self._client.predict(endpoint=self.endpoint, inputs={"input": txt, **kwargs})
embeddings.extend(r["embedding"] for r in resp["data"])
return embeddings
def embed_query(self, text: str, **kwargs) -> List[float]:
return self.embed_documents([text], **kwargs)[0]
```
So `test.py` changes to:
```python
print(embeddings.embed_query("hello", input_type="search_query"))
print(embeddings.embed_documents(["hello"], input_type="search_document"))
```
This might not be the best solution since it kind of defeats the purpose of separating `embed_query` and `embed_documents` for Cohere. Another solution is to subclass MlflowEmbeddings for Cohere (and others?).
I intend to open a PR with this change, so any guidance on the best approach is much appreciated!
### Expected behavior
The code should generate embeddings for the given words | {
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