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https://api.github.com/repos/hwchase17/langchain/issues/1828 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1828/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1828/comments | https://api.github.com/repos/hwchase17/langchain/issues/1828/events | https://github.com/hwchase17/langchain/pull/1828 | 1,632,666,243 | PR_kwDOIPDwls5MevLS | 1,828 | Fix typo in sqlite.ipynb | {
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} | [] | open | false | null | [] | null | [] | "2023-03-20T18:37:47" | "2023-03-20T18:37:47" | null | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/hwchase17/langchain/issues/1827 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1827/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1827/comments | https://api.github.com/repos/hwchase17/langchain/issues/1827/events | https://github.com/hwchase17/langchain/issues/1827 | 1,632,628,572 | I_kwDOIPDwls5hT-9c | 1,827 | Documentation not up to date | {
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} | [] | open | false | null | [] | null | [] | "2023-03-20T18:10:51" | "2023-03-20T18:10:51" | null | NONE | null | null | null | The current documentation https://langchain.readthedocs.io/en/latest/modules/agents/getting_started.html seems to not be up to date with version 0.0.117:
```shell
UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`
``` | {
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https://api.github.com/repos/hwchase17/langchain/issues/1826 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1826/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1826/comments | https://api.github.com/repos/hwchase17/langchain/issues/1826/events | https://github.com/hwchase17/langchain/pull/1826 | 1,632,605,604 | PR_kwDOIPDwls5Meh9B | 1,826 | gpt turbo patch DEMO | {
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} | [] | open | false | null | [] | null | [] | "2023-03-20T17:58:39" | "2023-03-20T17:58:39" | null | NONE | null | false | {
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} | Not a serious PR, just a demo.
Using the normal completions endpoint with Chat models.
With this patch, you can use the summarizing chains and agents with gpt-3.5-turbo and gpt-4. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1825 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1825/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1825/comments | https://api.github.com/repos/hwchase17/langchain/issues/1825/events | https://github.com/hwchase17/langchain/issues/1825 | 1,632,592,766 | I_kwDOIPDwls5hT2N- | 1,825 | Validation error- Metadata should not be empty or None | {
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I am experiencing an issue with Chroma:
`Chroma.from_texts(texts=chunks, embedding=embeddings, persist_directory=config.PERSIST_DIR, metadatas=None)`
opt/anaconda3/lib/python3.8/site-packages/langchain/vectorstores/chroma.py", line 27, in <listcomp>
(Document(page_content=result[0], metadata=result[1]), result[2])
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for Document
metadata
none is not an allowed value (type=type_error.none.not_allowed)
Thanks for all the help! | {
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https://api.github.com/repos/hwchase17/langchain/issues/1824 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1824/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1824/comments | https://api.github.com/repos/hwchase17/langchain/issues/1824/events | https://github.com/hwchase17/langchain/issues/1824 | 1,632,590,839 | I_kwDOIPDwls5hT1v3 | 1,824 | Avoiding recomputation of embeddings with Chroma | {
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} | [] | open | false | null | [] | null | [] | "2023-03-20T17:48:30" | "2023-03-20T17:48:30" | null | NONE | null | null | null | If I'm reading correctly, this is the function to add_texts to Chroma
```
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts (Iterable[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
Returns:
List[str]: List of IDs of the added texts.
"""
# TODO: Handle the case where the user doesn't provide ids on the Collection
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
embeddings = None
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(list(texts))
self._collection.add(
metadatas=metadatas, embeddings=embeddings, documents=texts, ids=ids
)
return ids
```
It does not seem to check if the texts are already inside the database. This means it's very easy to duplicate work when running indexing jobs incrementally. What's more, the Chroma class from langchain.vectorstores does not seem to expose functions to see if some text is already inside the vector store.
What's the preferred way of dealing with this? I can of course set up a separate db that keeps track of hashes of text inside the Chromadb, but this seems unnecessarily clunky and something that you'd expect the db to do for you.
| {
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https://api.github.com/repos/hwchase17/langchain/issues/1823 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1823/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1823/comments | https://api.github.com/repos/hwchase17/langchain/issues/1823/events | https://github.com/hwchase17/langchain/issues/1823 | 1,632,522,870 | I_kwDOIPDwls5hTlJ2 | 1,823 | New feature: Contribution | {
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} | [] | open | false | null | [] | null | [] | "2023-03-20T17:03:16" | "2023-03-20T17:03:16" | null | NONE | null | null | null | Hi,
I would like to contribute to LangChain, need to know if our feature is relevant as part of LangChain.
Would like check with you in private. How we can share our idea?
Moshe | {
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https://api.github.com/repos/hwchase17/langchain/issues/1822 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1822/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1822/comments | https://api.github.com/repos/hwchase17/langchain/issues/1822/events | https://github.com/hwchase17/langchain/issues/1822 | 1,632,466,855 | I_kwDOIPDwls5hTXen | 1,822 | Creating and using AzureChatOpenAI directly works fine, but crashing through ChatVectorDBChain with "ValueError: Should always be something for OpenAI." | {
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"This is fixed in [v0.0.117](https://github.com/hwchase17/langchain/releases/tag/v0.0.117)",
"Argh apologies, I updated it to that version but in wrong conda environment.\r\nSorry about that. Thanks a lot."
] | "2023-03-20T16:27:16" | "2023-03-20T16:47:11" | "2023-03-20T16:47:11" | NONE | null | null | null | Creating and using AzureChatOpenAI directly works fine, but crashing through ChatVectorDBChain with "ValueError: Should always be something for OpenAI."
Example:
```
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import ChatVectorDBChain
from langchain.document_loaders import PagedPDFSplitter
from langchain.chat_models import AzureChatOpenAI
from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemplate
from langchain.schema import AIMessage, HumanMessage, SystemMessage
system_template="""Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
{context}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}")
]
prompt = ChatPromptTemplate.from_messages(messages)
loader = PagedPDFSplitter("myfile.pdf")
text_splitter = CharacterTextSplitter(chunk_size=700, chunk_overlap=50)
documents = loader.load_and_split(text_splitter)
embeddings = OpenAIEmbeddings(chunk_size=1)
vectorstore = FAISS.from_documents(documents, embeddings)
qa = ChatVectorDBChain.from_llm(AzureChatOpenAI(temperature=0, deployment_name='gpt-35-turbo'), vectorstore, qa_prompt=prompt, top_k_docs_for_context=2)
## All good to here, but following line crashes:
result = qa({"question": "what is section 2 about?", "chat_history": []})
```
Crashes with:
ValueError: Should always be something for OpenAI.
This works fine on ChatOpenAI but not AzureChatOpenAI. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1821 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1821/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1821/comments | https://api.github.com/repos/hwchase17/langchain/issues/1821/events | https://github.com/hwchase17/langchain/pull/1821 | 1,632,461,114 | PR_kwDOIPDwls5MeDCR | 1,821 | allow FAISS object to merge with another FAISS object | {
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The self FAISS will be updated, while the target will remain untouched.
It will generate new UUID for docs in target FAISS (can be optional, but currently, it automatically generates new UUID). | {
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https://api.github.com/repos/hwchase17/langchain/issues/1818 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1818/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1818/comments | https://api.github.com/repos/hwchase17/langchain/issues/1818/events | https://github.com/hwchase17/langchain/pull/1818 | 1,632,212,776 | PR_kwDOIPDwls5MdNhz | 1,818 | Follow up to #1803 to remove dynamic docs route. | {
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} | [] | open | false | null | [] | null | [] | "2023-03-20T13:59:50" | "2023-03-20T14:00:22" | null | CONTRIBUTOR | null | null | null | Hey team,
i have created an API in the below format
`
@app.post("/get_answer")
async def get_answer(user_query):
return vector_db.qa(user_query)
`
And I call this function for 5 user_query, but it only able to process first query other query faces issues from OpenAI. Is calling OpenAI API parallely not allowed at all?
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https://api.github.com/repos/hwchase17/langchain/issues/1815 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1815/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1815/comments | https://api.github.com/repos/hwchase17/langchain/issues/1815/events | https://github.com/hwchase17/langchain/issues/1815 | 1,631,865,900 | I_kwDOIPDwls5hREws | 1,815 | During the tracing of ChatVectorDBChain, even though it shows source_documents but when clicked on Explore, source_documents suddenly becomes [object Object],[object Object] | {
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"are you using ChatOpenAI model? or normal OpenAI model?",
"Currently, we are using AzureOpenAI **text-davinci-003** model"
] | "2023-03-20T10:58:37" | "2023-03-20T17:41:15" | null | NONE | null | null | null | During the tracing of ChatVectorDBChain, even though it shows source_documents but when clicked on Explore, source_documents suddenly becomes [object Object],[object Object].
Here is an example:
and then after clicking on explore button:
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https://api.github.com/repos/hwchase17/langchain/issues/1813 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1813/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1813/comments | https://api.github.com/repos/hwchase17/langchain/issues/1813/events | https://github.com/hwchase17/langchain/pull/1813 | 1,631,445,824 | PR_kwDOIPDwls5MankV | 1,813 | add from to add texts from embeddings | {
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} | # Why
- Since making vectors of texts can be done outside of langchain Faiss, this PR is to add functionality to pass text and its vector pair to initialize and add embedding to Faiss.
# What
- Add `from_embedding` method in Faiss to initialize Faiss index by passing the embeddings paired with original text made outside of the langchain.
- Add `add_embedding` method to add embedding paired with original text to append the embedding made outside of the langchain. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1812 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1812/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1812/comments | https://api.github.com/repos/hwchase17/langchain/issues/1812/events | https://github.com/hwchase17/langchain/pull/1812 | 1,631,442,344 | PR_kwDOIPDwls5MamyV | 1,812 | fix: AzureChatOpenAI take base, version and type from model_kwargs | {
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} | Currently **openai_api_base**, **openai_api_version**, **openai_api_type** are not part of required fields of ChatOpenAI, So these fields are part of **model_kwargs**. This PR fix the issue #1810 | {
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now If we're embedding a big document in chunks, it tends to fail at some point and Pinecone.from_texts() does not have exception handling, on failure of a chunk whole document that is left is wasted.
<img width="641" alt="image" src="https://user-images.githubusercontent.com/64721638/226257394-76ba84f7-2801-49e9-9261-b4a4550a4660.png">
- we should use exponential retry or some better exception handling so that whole document does not crashes.
please let me know if I can work on the issue and submit a PR | {
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https://api.github.com/repos/hwchase17/langchain/issues/1810 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1810/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1810/comments | https://api.github.com/repos/hwchase17/langchain/issues/1810/events | https://github.com/hwchase17/langchain/issues/1810 | 1,631,427,537 | I_kwDOIPDwls5hPZvR | 1,810 | AzureChatOpenAI failed to accept openai_api_base, throwing error | {
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Usage:
I currently using **AzureChatOpenAI** with below parameters
But it is not taking the value from parameter and throwing error. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1809 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1809/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1809/comments | https://api.github.com/repos/hwchase17/langchain/issues/1809/events | https://github.com/hwchase17/langchain/issues/1809 | 1,631,399,350 | I_kwDOIPDwls5hPS22 | 1,809 | issue with pinecone similarity_search function | {
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} | [] | open | false | null | [] | null | [] | "2023-03-20T05:13:20" | "2023-03-20T05:37:20" | null | NONE | null | null | null | While I am trying to rebuild chat_pdf based on mayo's example.
I noticed that vector store with pinecone doesn't respond with similar docs when it performs similarity_search function.
I test it against other vector provider like faiss and chroma. In both cases, those all works.
Here is the [code link](https://github.com/butzhang/simple_chat_pdf/blob/main/simple_chat_pdf/components/question_handler.py#L28-L30)
Steps to reproduce.
"""
from simple_chat_pdf.components.question_handler import QuestionHandler
question = 'what is this legal case about'
r = QuestionHandler().get_answer(question=question, chat_history=[])
"""
You can switch from different vector and see only pinecone provide failed and didn't find similar docs
you need to switch to your open_api key because mine might expire
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"Just came here to report this. The current master (b6ba989) test_openai integration test is failing, and this should fix it! Thanks\r\n\r\n\r\n"
] | "2023-03-20T03:27:12" | "2023-03-20T14:51:20" | "2023-03-20T14:51:19" | OWNER | null | false | {
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https://api.github.com/repos/hwchase17/langchain/issues/1805 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1805/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1805/comments | https://api.github.com/repos/hwchase17/langchain/issues/1805/events | https://github.com/hwchase17/langchain/issues/1805 | 1,631,276,872 | I_kwDOIPDwls5hO09I | 1,805 | Document loader for Azure Blob storage | {
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"LGTM!"
] | "2023-03-20T00:12:04" | "2023-03-20T03:12:34" | "2023-03-20T03:12:33" | COLLABORATOR | null | false | {
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} | From Robert "Right now the dynamic/ route for specifically the above endpoints is acting on all providers a user has set up, not just the provider for the supplied API key." | {
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https://api.github.com/repos/hwchase17/langchain/issues/1801 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1801/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1801/comments | https://api.github.com/repos/hwchase17/langchain/issues/1801/events | https://github.com/hwchase17/langchain/issues/1801 | 1,631,163,256 | I_kwDOIPDwls5hOZN4 | 1,801 | Poetry 1.4.0 installation fails | {
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"Cut a PR upstream to fix `pydata-sphinx-theme` wheels: https://github.com/pradyunsg/sphinx-theme-builder/pull/39"
] | "2023-03-19T23:42:55" | "2023-03-20T01:54:33" | null | NONE | null | null | null | `poetry install -E all` fails with Poetry >=1.4.0 due to upstream incompatibility between `poetry>=1.4.0` and `pydata_sphinx_theme`.
This is a tracking issue. I've already created an issue upstream here: https://github.com/pydata/pydata-sphinx-theme/issues/1253 | {
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https://api.github.com/repos/hwchase17/langchain/issues/1800 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1800/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1800/comments | https://api.github.com/repos/hwchase17/langchain/issues/1800/events | https://github.com/hwchase17/langchain/issues/1800 | 1,631,160,642 | I_kwDOIPDwls5hOYlC | 1,800 | Error when overriding default prompt template of ConversationChain | {
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"yes you're correct - conversation chain only currently allows for a single input, that being the input key, and then also history (coming from memory)",
"How should I handle this situation if I want to add _context_?\r\n\r\n```\r\nsystem_template=\"\"\"Use the following pieces of context to answer the users question. \r\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\r\n----------------\r\n{context}\"\"\"\r\nmessages = [\r\n SystemMessagePromptTemplate.from_template(system_template),\r\n MessagesPlaceholder(variable_name=\"history\"),\r\n HumanMessagePromptTemplate.from_template(\"{input}\")\r\n]\r\n```"
] | "2023-03-19T23:33:20" | "2023-03-20T14:50:20" | null | NONE | null | null | null | Hi, does anyone know how to override the prompt template of ConversationChain? I am creating a custom prompt template that takes in an additional input variable
```
PROMPT_TEMPLATE = """ {my_info}
{history}
Human: {input}
AI:"""
PROMPT = PromptTemplate(
input_variables=["history", "input", "my_info"], template=PROMPT_TEMPLATE
)
conversation_chain = ConversationChain(
prompt=PROMPT,
llm=OpenAI(temperature=0.7),
verbose=True,
memory=ConversationBufferMemory()
)
```
but got the following error:
```
Got unexpected prompt input variables. The prompt expects ['history', 'input', 'my_info'], but got ['history'] as inputs from memory, and input as the normal input key. (type=value_error)
```
Is my understanding correct that currently ConversationChain can only support prompt template that takes in "history" and "input" as the input variables? | {
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https://api.github.com/repos/hwchase17/langchain/issues/1799 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1799/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1799/comments | https://api.github.com/repos/hwchase17/langchain/issues/1799/events | https://github.com/hwchase17/langchain/issues/1799 | 1,631,156,471 | I_kwDOIPDwls5hOXj3 | 1,799 | v0.0.116 ChatVectorDBChain with ChatOpenAI fails with `Should always be something for OpenAI.` | {
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"Yes, facing the same issue with `QA with Sources` Chain as well for both `ChatOpenAI` and `AzureChatOpenAI`.",
"ah sorry - will fix"
] | "2023-03-19T23:18:24" | "2023-03-20T14:51:20" | "2023-03-20T14:51:20" | CONTRIBUTOR | null | null | null | `ChatVectorDBChain` with `ChatOpenAI` fails with `Should always be something for OpenAI.`
It appears `_combine_llm_outputs` added to `ChatOpenAI` in v0.0.116 is receiving a `llm_outputs` list containing a `None` value. I think this is related to streaming responses from the OpenAI API.
see https://github.com/hwchase17/langchain/pull/1785 | {
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https://api.github.com/repos/hwchase17/langchain/issues/1798 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1798/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1798/comments | https://api.github.com/repos/hwchase17/langchain/issues/1798/events | https://github.com/hwchase17/langchain/pull/1798 | 1,631,150,045 | PR_kwDOIPDwls5MZoik | 1,798 | Add request timeout to ChatOpenAI | {
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} | [] | closed | false | null | [] | null | [] | "2023-03-19T22:57:08" | "2023-03-20T03:19:42" | "2023-03-20T03:19:42" | CONTRIBUTOR | null | false | {
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} | Add request_timeout field to ChatOpenAI. Defaults to 60s. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1797 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1797/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1797/comments | https://api.github.com/repos/hwchase17/langchain/issues/1797/events | https://github.com/hwchase17/langchain/issues/1797 | 1,631,141,587 | I_kwDOIPDwls5hOT7T | 1,797 | [Design Proposal] Standardized interface for LLM provider usage | {
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"thanks for this thoughtful proposal, really appreciate the clarity. I think standardizing model/model_name is a quick easy win.\r\n\r\nof the other parts, what do you think the highest value ones are?",
"@hwchase17 I think model discovery (use-case 2) is the next greatest value-add, though it is also the most ambiguous and tricky to implement. Although the scope of this proposal is large, I think it's small enough to be doable in a single PR. Since each of these suggestions requires implementing this for every LLM provider anyways, splitting the implementation of this proposal across multiple PRs seems to involve more complexity than not."
] | "2023-03-19T22:28:09" | "2023-03-20T04:01:36" | null | NONE | null | null | null | @hwchase17 Thank you for leading work on this repo! It's very clear you've put a lot of love into this project. 🤗 ❤️
My coworker, @3coins, with whom I work daily, is a regular contributor here.
I wanted to offer you a design proposal that I think would be a great addition to LangChain. Here goes:
# Motivation
LLM providers lack a unified interface for common use-cases:
- **Specifying a model ID to a model provider**
- Users lack a unified interface for specifying the model they wish to use when instantiating a model provider. `openai` and `openai-chat` both expect the model ID in `model_name`, but `ai21` and `anthropic` expect `model`.
- **Discovering supported models**
- Users don't have any way of determining valid model IDs for a given provider. For example, a user trying to invoke ChatGPT via `OpenAI(model_name="chatgpt")` will be quite confused, and there's no clear way for a user to look up what the allowed values are.
- The only solution for this now is to look up upstream API reference documentation on what models are allowed, which is very tedious.
- **Determining prerequisites**
- Users don't have any way of determining what packages they need without looking at the source code or encountering a runtime `ImportError`, even if they have knowledge of what providers they wish to use. There's no attribute/property on any of the model providers that lists what packages must be installed before using them.
- **Determining necessary authentication**
- Similar to the use-case above, users don't have any way of knowing in advance what authentication they should supply and how to supply them before using a provider.
We have run into this pain points while [integrating LangChain with Jupyter AI](https://github.com/jupyterlab/jupyter-ai/pull/18). This proposal, if accepted, would allow for applications to build on top of LangChain LLM providers much more easily.
# Proposal
I propose:
1. We formalize the notion of model IDs and provider IDs, and enforce this naming convention broadly throughout the codebase and documentation.
2. We implement a unified interface for provider interaction by expanding the definition of `BaseLanguageModel`:
```python
class EnvAuthStrategy(BaseModel):
type: Literal["env"] = 'env'
name: str
# for sagemaker_endpoint expecting creds at ~/.aws/credentials
class FileAuthStrategy(BaseModel):
type: Literal["file"] = 'file'
path: str
AuthStrategy = Union[EnvAuthStrategy, FileAuthStrategy]
class BaseLanguageModel(BaseModel, ABC):
# provider ID. this is currently bound to _llm_type()
id: str
# ID of the model to invoke by this provider
model_id: str
# List of supported models
# For registry providers, this will just be ["*"]
# See rest of the issue for explanation
models: List[str]
# List of package requirements
package_reqs: List[str]
# Authn/authz strategy
auth_strategy: AuthStrategy
...
```
Subclasses should override these fields and [make all of them constant](https://docs.pydantic.dev/usage/schema/#field-customization) with the exception of `model_id`. For example, the `cohere` provider might look something like this:
```python
class CohereAuthStrategy(EnvAuthStrategy):
name = "COHERE_API_KEY"
class Cohere(LLM, BaseModel):
id = Field("cohere", const=True)
# "medium" is just the default, still changeable at runtime
model_id = "medium"
# Cohere model provider supports any model available via
# `cohere.Client#generate()`.`
# Reference: https://docs.cohere.ai/reference/generate
models: Field(["medium", "xlarge"], const=True)
package_reqs: Field(["cohere"], const=True)
auth_strategy: Field(CohereAuthStrategy, const=True)
```
This strategy also handles registry providers (see "Terminology" section below) nicely. **A model ID can be defined as any identifier for an instance of a model**. This is best illustrated with a few examples:
- `huggingface_hub`: the HF repo ID, e.g. `gpt2` or `google/flan-t5-xxl`
- `huggingface_endpoint`: the URL to the HF endpoint, e.g. `https://foo.us-west-2.aws.endpoints.huggingface.cloud/bar`
- `sagemaker_endpoint`: the endpoint name. Your region and authentication credentials should already be specified via boto3.
While the syntax of model IDs is indeed very different for registry providers, if you think about it, it still functions exactly as a model ID: it identifies one and only one model hosted elsewhere.
Because registry providers have a dynamic, unknown, and very large set of valid model IDs, they declare their supported models using a wildcard like so:
```
models: Field(["*"], const=True)
```
# Next steps
Let's first get consensus on this proposal and assign the work later. I'd be happy to kick off the PR and define the types, but I'll likely need to loop in other contributors to assist due to the scope of this proposal. There will also need to be some work done on the documentation as well.
# Appendix
## Terminology
- **Model provider / LLM provider**: A class that can provide one or more models. In LangChain, these currently inherit `BaseLanguageModel`.
- **Registry provider**: A special subset of model providers that have a dynamic number of provided models.
- These include but are not limited to: `huggingface_hub`, `huggingface_endpoint`, `sagemaker_endpoint`
- These providers are unique in the sense that they do not have a static number of models they support. New models are being uploaded to HuggingFace Hub daily.
- We call these providers "registry providers" (since they mimic the behavior of a package registry, like NPM/PyPi)
- **Model**: A model provided by a model provider (forgive the circular working definition, models are quite difficult to define strictly). For example, `text-davinci-003` is a model provided by the `openai` model provider.
- **Model provider ID**: a string identifier for a model provider. Currently this is retrieved from the `_llm_type` property.
- **Model ID**: a string identifier for a model.
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https://api.github.com/repos/hwchase17/langchain/issues/1796 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1796/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1796/comments | https://api.github.com/repos/hwchase17/langchain/issues/1796/events | https://github.com/hwchase17/langchain/issues/1796 | 1,631,129,215 | I_kwDOIPDwls5hOQ5_ | 1,796 | OpenAI base model initialization ignores currently set openi.api_key | {
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} | [] | open | false | null | [] | null | [] | "2023-03-19T21:45:28" | "2023-03-19T21:45:28" | null | CONTRIBUTOR | null | null | null | The current design leaves only two options for initialization `openai-python`:
1. `OPENAI_API_KEY` environment variable
2. pass in `openai_api_key`
LangChain works just fine with the other `openai-python` settings like `openai.api_type`.
Can we make it so it uses `openai.api_key` if it is not `None`?
Happy to try a PR but wanted to understand design choice first.
https://github.com/hwchase17/langchain/blob/master/langchain/llms/openai.py#LL202C9-L202C29 | {
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"This looks like an issue with the upstream API and not LangChain itself. LangChain is not responsible for upstream API availability or rate limiting policies.",
"@dlqqq How can I set a limit for response time? I'm using `signal` library from python. Wondering If there are alternative ways",
"@mzhadigerov Unfortunately, a quick glance at the `pinecone.py` source seems to show that there isn't timeout support at the moment for Pinecone. The community is still working to bring timeouts to classes throughout the repo: https://github.com/hwchase17/langchain/pull/1798"
] | "2023-03-19T21:35:50" | "2023-03-20T01:59:37" | null | NONE | null | null | null | I'm using the pipeline for Q&A pipeline on non-english language:
```
pinecone.init(
api_key=PINECONE_API_KEY, # find at app.pinecone.io
environment=PINECONE_API_ENV # next to api key in console
)
index_name = "langchain2"
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
docsearch = Pinecone.from_existing_index(index_name=index_name, embedding=embeddings)
llm = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)
chain = load_qa_chain(llm, chain_type="stuff")
query = "QUESTION"
docs = docsearch.similarity_search(query, include_metadata=True)
res = chain.run(input_documents=docs, question=query)
print(res)
```
It stucks in `res = chain.run(input_documents=docs, question=query)` lane. I'm waiting for ~20 mins already. What's the reason for that and how to investigate?
---------------
**UPD**
After ~30 mins I got
```
Retrying langchain.llms.openai.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised Timeout: Request timed out: HTTPSConnectionPool(host='api.openai.com', port=443): Read timed out. (read timeout=600).
```
What's the reason for that and how can I fix it? | {
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https://api.github.com/repos/hwchase17/langchain/issues/1794 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1794/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1794/comments | https://api.github.com/repos/hwchase17/langchain/issues/1794/events | https://github.com/hwchase17/langchain/pull/1794 | 1,631,124,979 | PR_kwDOIPDwls5MZjpQ | 1,794 | update the LLM math prompt to use simpler logic | {
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} | [] | closed | false | null | [] | null | [] | "2023-03-19T21:28:58" | "2023-03-20T03:44:07" | "2023-03-20T03:44:07" | CONTRIBUTOR | null | false | {
"url": "https://api.github.com/repos/hwchase17/langchain/pulls/1794",
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} | Existing `LLMMathChain` is a bit unpredictable in its behavior. I suspect that it’s because the prompt allows the model to generate the answer bypassing python code altogether [here](https://github.com/hwchase17/langchain/blob/df8702fead4a26263f8a3f0a6fc36db1a8c37c7b/langchain/chains/llm_math/prompt.py#L19), and it can occasionally hallucinate an answer.
This PR introduces a simplified version of the prompt. The new version improves the behavior in the following ways:
- more robust to the variations in the input format (literal values / spelled out values / whitespacing, etc)
- smaller prompt size -> cheaper queries
**CAUTION**⚠️: both the new and the old prompts suffer from injection attacks and are quite dangerous since both are executing Python code blindly. In the future this chain would benefit from using a much more constrained executor
See examples below that compare old vs new behavior:
```python
test_cases = [
# input, expected output
("5", 5), # same value
("5 + 3", 8), # trivial math
("2^3.171", 2 ** 3.171), # insensitivity to the spaces around operator
(" 2 ^3.171 ", 2 ** 3.171), # insensitivity to whitespacing overall
("two to the power of three point one hundred seventy one", 2 ** 3.171), # spelled out values and operations
("five + three squared minus 1", 5 + 3 ** 2 - 1), # mixed literal and spelled out values / operations
("2097 times 27.31", 2097 * 27.31), # mixed types
("two thousand ninety seven times twenty seven point thirty one", 2097 * 27.31), # spelled out mixed types
("209758 / 2714", 209758 / 2714), # int division
("209758.857 divided by 2714.31", 209758.857 / 2714.31) # float division
]
for input_text, expected_output in test_cases:
print("input: ", input_text)
print("expected output :", expected_output)
print("old chain: ", math_chain.run(input_text))
print("new chain: ", new_math_chain.run(input_text), "\n")
```
```
input: 5
expected output : 5
old chain: Answer: 8
new chain: Answer: 5
input: 5 + 3
expected output : 8
old chain: Answer: 8
new chain: Answer: 8
input: 2^3.171
expected output : 9.006708689094099
old chain: Answer: 10.945
new chain: Answer: 9.006708689094099
input: 2 ^3.171
expected output : 9.006708689094099
old chain: Answer: 10.945
new chain: Answer: 9.006708689094099
input: two to the power of three point one hundred seventy one
expected output : 9.006708689094099
old chain: Answer: 9.9078598877
new chain: Answer: 9.006708689094099
input: five + three squared minus 1
expected output : 13
old chain: Answer: 19
new chain: Answer: 13
input: 2097 times 27.31
expected output : 57269.07
old chain: Answer: 57239.07
new chain: Answer: 57269.07
input: two thousand ninety seven times twenty seven point thirty one
expected output : 57269.07
old chain: Answer: 56753.97
new chain: Answer: 57269.07
input: 209758 / 2714
expected output : 77.28739867354459
old chain: Answer: 77.2
new chain: Answer: 77.28739867354459
input: 209758.857 divided by 2714.31
expected output : 77.27888745205964
old chain: Answer: 77.27888745205964
new chain: Answer: 77.27888745205964
``` | {
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https://api.github.com/repos/hwchase17/langchain/issues/1793 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1793/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1793/comments | https://api.github.com/repos/hwchase17/langchain/issues/1793/events | https://github.com/hwchase17/langchain/issues/1793 | 1,631,119,009 | I_kwDOIPDwls5hOOah | 1,793 | [BUG] Chroma DB - similarity_search - chromadb.errors.NotEnoughElementsException | {
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"Facing the same issue. Is it possible to pass the minimum number of query results it can return from our end?",
"Facing the same issue"
] | "2023-03-19T21:11:37" | "2023-03-20T15:10:53" | null | NONE | null | null | null | Within the Chroma DB, similarity_search has a default "k" of 4.
However, if there are less than 4 results to return to the query, it will crash instead of returning whatever is available.
```similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[langchain.docstore.document.Document][[source]](https://langchain.readthedocs.io/en/latest/_modules/langchain/vectorstores/chroma.html#Chroma.similarity_search)
Run similarity search with Chroma.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of documents most simmilar to the query text.
Return type
List[Document]
```
```
raise NotEnoughElementsException(
chromadb.errors.NotEnoughElementsException: Number of requested results 4 cannot be greater than number of elements in index 1
```
I believe a check is needed to return whatever is available whenever the results are < k.
Edit: You can set `k=1` for the search, but then you are artificially limiting yourself, without knowing what is available.
It really should check if the N results returned are <= K, and if so, instead of crashing it should return whatever is availab.e. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1792 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1792/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1792/comments | https://api.github.com/repos/hwchase17/langchain/issues/1792/events | https://github.com/hwchase17/langchain/issues/1792 | 1,631,113,414 | I_kwDOIPDwls5hONDG | 1,792 | Query on existing index | {
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"Okay, seems like sending `index_name` and `embedding` is enough.",
"text_key is the key in the metadata where the text associated with the embeddings is stored. namespace is optional, depends if you are using it or not\r\n\r\n"
] | "2023-03-19T20:55:14" | "2023-03-20T03:35:45" | null | NONE | null | null | null | How to query from an existing index?
I filled up an index in Pinecode using:
```
docsearch = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name)
```
Now, I'm creating a separate `.py` file, where I need to use the existing index to query. As I understand I need to use the following function:
```
@classmethod
def from_existing_index(
cls,
index_name: str,
embedding: Embeddings,
text_key: str = "text",
namespace: Optional[str] = None,
) -> Pinecone:
"""Load pinecone vectorstore from index name."""
try:
import pinecone
except ImportError:
raise ValueError(
"Could not import pinecone python package. "
"Please install it with `pip install pinecone-client`."
)
return cls(
pinecone.Index(index_name), embedding.embed_query, text_key, namespace
)
```
but what are the arguments there? I know only my `index_name` . What are the rest arguments? Embeddings are embeddings from OpenAI, right?
e.g.:
```
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
```
What is the `text_key`? And what `name_space` ? | {
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https://api.github.com/repos/hwchase17/langchain/issues/1791 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1791/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1791/comments | https://api.github.com/repos/hwchase17/langchain/issues/1791/events | https://github.com/hwchase17/langchain/pull/1791 | 1,631,111,699 | PR_kwDOIPDwls5MZg9u | 1,791 | Corrected small typo in error message. | {
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} | [] | closed | false | null | [] | null | [] | "2023-03-19T20:49:01" | "2023-03-20T14:51:36" | "2023-03-20T14:51:36" | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/hwchase17/langchain/issues/1790 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1790/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1790/comments | https://api.github.com/repos/hwchase17/langchain/issues/1790/events | https://github.com/hwchase17/langchain/issues/1790 | 1,631,067,675 | I_kwDOIPDwls5hOB4b | 1,790 | [BUG] OpenMetero prompt token count error | {
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} | [] | open | false | null | [] | null | [] | "2023-03-19T18:40:49" | "2023-03-19T18:40:49" | null | NONE | null | null | null | # [BUG] OpenMetero prompt token count error
The OpenMetero api returns a json that is large and the GPT-3 api cannot study it to return information
## What and Why?
While running the OpenMetero Lang chain the open metero api returns a large json file that is then sent to the gpt-3 api to understand, which is larger than the token size thus returning errors.
## Suggestions?
Add another step to parse json using json chain | {
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"thanks!"
] | "2023-03-19T17:49:21" | "2023-03-20T01:18:24" | "2023-03-19T18:01:04" | CONTRIBUTOR | null | false | {
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"merged_at": "2023-03-19T18:01:04"
} | After the changes in #1783, `summary_message_role` is no longer used in `ConversationSummaryBufferMemory`, so this PR removes it. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1788 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1788/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1788/comments | https://api.github.com/repos/hwchase17/langchain/issues/1788/events | https://github.com/hwchase17/langchain/pull/1788 | 1,631,047,284 | PR_kwDOIPDwls5MZUN1 | 1,788 | bump version to 0.0.116 | {
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https://api.github.com/repos/hwchase17/langchain/issues/1779 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1779/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1779/comments | https://api.github.com/repos/hwchase17/langchain/issues/1779/events | https://github.com/hwchase17/langchain/issues/1779 | 1,630,987,862 | I_kwDOIPDwls5hNuZW | 1,779 | Fix regex for map_rerank_prompt.py | {
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This regex is not handling the case where if answer text contains something like: `\n Helpful Score: 100` and the parsing fails even if the score is 100.
It should be something like: `(.*?)\n((.?)*)Score: (.*)` to resolve the use case | {
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https://api.github.com/repos/hwchase17/langchain/issues/1778 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1778/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1778/comments | https://api.github.com/repos/hwchase17/langchain/issues/1778/events | https://github.com/hwchase17/langchain/pull/1778 | 1,630,986,992 | PR_kwDOIPDwls5MZH19 | 1,778 | GPT-4 support | {
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https://api.github.com/repos/hwchase17/langchain/issues/1777 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1777/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1777/comments | https://api.github.com/repos/hwchase17/langchain/issues/1777/events | https://github.com/hwchase17/langchain/issues/1777 | 1,630,984,304 | I_kwDOIPDwls5hNthw | 1,777 | Alpaca (Fine-tuned LLaMA) | {
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"Also worth considering Instruct-tuned LLaMA models that can run on consumer hardware, such as \r\nhttps://github.com/tloen/alpaca-lora. ",
"A basic LangChainJS package for Alpaca is here https://github.com/linonetwo/langchain-alpaca"
] | "2023-03-19T15:16:09" | "2023-03-20T18:47:20" | null | NONE | null | null | null | It would be great to see LangChain integrate with Standford's Alpaca 7B model, a fine-tuned LlaMa (see https://github.com/hwchase17/langchain/issues/1473).
Standford created an AI able to generate outputs that were largely on par with OpenAI’s `text-davinci-003` and regularly better than GPT-3 — all for a fraction of the computing power and price.
Stanford's Alpaca is a language model that was fine-tuned from Meta's LLaMA with 52,000 instructions generated by GPT-3.5[[1]](https://the-decoder.com/stanfords-alpaca-shows-that-openai-may-have-a-problem/). The researchers used AI-generated instructions to train Alpaca 7B, which exhibits many GPT-3.5-like behaviors[[1]](https://the-decoder.com/stanfords-alpaca-shows-that-openai-may-have-a-problem/). In a blind test using input from the Self-Instruct Evaluation Set, both models performed comparably[[1]](https://the-decoder.com/stanfords-alpaca-shows-that-openai-may-have-a-problem/)[[2]](https://github.com/tatsu-lab/stanford_alpaca). However, Alpaca has problems common to other language models such as hallucinations, toxicity, and stereotyping[[1]](https://the-decoder.com/stanfords-alpaca-shows-that-openai-may-have-a-problem/). The team is releasing an interactive demo, the training dataset, and the training code for research purposes[[1]](https://the-decoder.com/stanfords-alpaca-shows-that-openai-may-have-a-problem/)[[4]](https://crfm.stanford.edu/2023/03/13/alpaca.html).
Alpaca is still under development and has limitations that need to be addressed. The researchers have not yet fine-tuned the model to be safe and harmless[[2]](https://github.com/tatsu-lab/stanford_alpaca). They encourage users to be cautious when interacting with Alpaca and report any concerning behavior to help improve it[[2]](https://github.com/tatsu-lab/stanford_alpaca).
LLaMA is a new open-source language model from Meta Research that performs as well as closed-source models. Stanford's Alpaca is a fine-tuned version of LLaMA that can respond to instructions like ChatGPT[[3]](https://replicate.com/blog/replicate-alpaca). It functions more like a fancy version of autocomplete than a conversational bot[[3]](https://replicate.com/blog/replicate-alpaca).
The researchers are releasing their findings about an instruction-following language model dubbed Alpaca. They trained the Alpaca model on 52K instruction-following demonstrations generated in the style of self-instruct using `text-davinci-003`. On the self-instruct evaluation set, Alpaca shows many behaviors similar to OpenAI’s `text-davinci-003` but is also surprisingly small and easy/cheap to reproduce[[4]](https://crfm.stanford.edu/2023/03/13/alpaca.html). | {
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} | [] | closed | false | null | [] | null | [] | "2023-03-19T13:34:43" | "2023-03-19T16:28:57" | "2023-03-19T16:28:57" | CONTRIBUTOR | null | false | {
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} | Hi, I just want to add a PR on the prompt serialization examples of loading from JSON so that it can contain the same as loading from YAML. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1774 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1774/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1774/comments | https://api.github.com/repos/hwchase17/langchain/issues/1774/events | https://github.com/hwchase17/langchain/issues/1774 | 1,630,889,769 | I_kwDOIPDwls5hNWcp | 1,774 | How to use self defined variable in an Agent? | {
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} | [] | open | false | null | [] | null | [] | "2023-03-19T11:03:28" | "2023-03-19T11:03:28" | null | NONE | null | null | null | This issue may sound silly, I apologize for not being able to find the answer (and for my poor English).
For example, I want my chatbot only use paths from a list I give. I tried to include the following content in suffix:
```py
"""
omit...
You should only use the following file paths:
{path_list}
omit...
"""
```
But how can I add `path_list` to `input_variables`? I noticed that the `create_prompt` method has the following statement:
```py
if input_variables is None:
input_variables = ["input", "chat_history", "agent_scratchpad"]
```
So I tried adding `input_variables` in `agent_kwargs`:
```py
agent = initialize_agent(
tools,
llm,
agent="conversational-react-description",
verbose=True,
memory=memory,
return_intermediate_steps=True,
agent_kwargs={
'prefix': CHAT_BOT_PREFIX,
'format_instructions': CHAT_BOT_FORMAT_INSTRUCTIONS,
'suffix': CHAT_BOT_SUFFIX,
'input_variables': ["input", "chat_history", "agent_scratchpad", "path_list"], # here
},
)
```
This does create a prompt in the agent with a `path_list` variable in it. But how can I pass the value of path_list?
The way I thought was to pass it togethor with `input`.
```py
result = agent({"input": str_input, "path_list": path_list_str})
```
But this doesn't work. It raises the following error:
```text
ValueError: One input key expected got ['path_list', 'input']
```
I don't know if there's another way or if I'm missing something. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1773 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1773/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1773/comments | https://api.github.com/repos/hwchase17/langchain/issues/1773/events | https://github.com/hwchase17/langchain/pull/1773 | 1,630,869,854 | PR_kwDOIPDwls5MYwGC | 1,773 | added function for renaming variables | {
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"clever, but some issues\r\n\r\n1) the change to the template depends on the format being used (eg what you did is f-string specific)\r\n\r\n2) Id also rather do the renaming outside the constructors - eg have the user get the prompt, renaming the variable, then pass it it. will just keep it cleaner/more scalable\r\n\r\ni have an idea for how to do this tho! because i do like the idea. so let me take a stab",
"@hwchase17 okay sure, feel free to let me know what to do to save your time implementing, happy either way!"
] | "2023-03-19T10:12:48" | "2023-03-20T06:11:04" | null | CONTRIBUTOR | null | false | {
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} | I came into the issue when using the `VectorDBQAWithSourcesChain` that because of the [example prompt](https://github.com/hwchase17/langchain/blob/master/langchain/chains/qa_with_sources/stuff_prompt.py) being used, it meant my source metadata in the vector DB (pinecone) had to be named `"source"`.
I and other users can go back and rename the document IDs, URLs, etc as `"source"` but it seems like an unnecessary restriction. Instead, being able to swap `"source"` for `"url"`, `"filename"`, etc seems like a better solution to me.
With that in mind, I'd like to add a new method to prompt templates called `rename_variable`, which simply takes an existing variable and a new variable name as input, and formats the prompt template to use the new variable name.
In the example of using `VectorDBQAWithSourcesChain`, this looks like:
```python
qa = VectorDBQAWithSourcesChain.from_chain_type(
llm=llm,
chain_type='stuff',
vectorstore=vectorstore
)
qa.combine_documents_chain.document_prompt.rename_variable(
'source', 'pdf_url'
)
```
And if we view the `qa.combine_documents_chain.document_prompt` we'll see:
```
PromptTemplate(input_variables=['page_content', 'pdf_url'], output_parser=None, partial_variables={}, template='Content: {page_content}\nSource: {pdf_url}', template_format='f-string', validate_template=True)
```
Alternatively, I added a `sources_variable` parameter to the initialization calls for `VectorDBQAWithSourcesChain`, so the following can be run with the same result:
```python
qa = VectorDBQAWithSourcesChain.from_chain_type(
llm=llm,
chain_type='stuff',
vectorstore=vectorstore,
sources_variable="pdf_url"
)
``` | {
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https://api.github.com/repos/hwchase17/langchain/issues/1771 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1771/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1771/comments | https://api.github.com/repos/hwchase17/langchain/issues/1771/events | https://github.com/hwchase17/langchain/pull/1771 | 1,630,767,125 | PR_kwDOIPDwls5MYbdF | 1,771 | Allow `max_tokens`=-1 for `ChatOpenAI` and set the default value to -1 | {
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"https://github.com/hwchase17/langchain/pull/1782",
"Yeah it seems working well. Thank you for your feedback.\r\nI will close this PR now."
] | "2023-03-19T03:41:46" | "2023-03-19T17:53:39" | "2023-03-19T17:23:16" | CONTRIBUTOR | null | false | {
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} | Resolves #1532, resolves #1652.
In the current implementation of `ChatOpenAI`, the default value for max_tokens is set to 256, and it cannot take a value of -1.
In this PR, we will allow `max_tokens` to take a value of -1. When `max_tokens`=-1, the `max_tokens` parameter will be omitted, which means there will be no token limit for the response.
Originally, the default value of `max_tokens` was set to 256, causing longer responses to be cut off and creating confusion (as in issue #1652). This PR sets the default value of `max_tokens` to -1, preventing cutoffs from occurring.
The solution was originally proposed by @adlindenberg in the issue discussion (#1532). Thank you, @adlindenberg, for providing the idea for this fix. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1770 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1770/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1770/comments | https://api.github.com/repos/hwchase17/langchain/issues/1770/events | https://github.com/hwchase17/langchain/pull/1770 | 1,630,755,767 | PR_kwDOIPDwls5MYZAP | 1,770 | Fix wrong prompt in refine chain | {
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"@hwchase17 can you take a look? thanks!"
] | "2023-03-19T03:11:10" | "2023-03-20T14:29:50" | "2023-03-19T17:03:45" | CONTRIBUTOR | null | false | {
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} | I got this during testing
```
ValueError: Missing some input keys: {'existing_answer'}
```
Upon review, the initial prompt should be `QUESTION_PROMPT_SELECTOR`. | {
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https://api.github.com/repos/hwchase17/langchain/issues/1769 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1769/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1769/comments | https://api.github.com/repos/hwchase17/langchain/issues/1769/events | https://github.com/hwchase17/langchain/pull/1769 | 1,630,748,771 | PR_kwDOIPDwls5MYXpA | 1,769 | WIP Jeremy/guardrails | {
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} | opening so i can leave comments | {
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https://api.github.com/repos/hwchase17/langchain/issues/1768 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1768/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1768/comments | https://api.github.com/repos/hwchase17/langchain/issues/1768/events | https://github.com/hwchase17/langchain/pull/1768 | 1,630,740,499 | PR_kwDOIPDwls5MYWCL | 1,768 | feat: SimpleParallelChain | {
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"Oops, apologies. It should be fixed now. We should expect four files to have changed."
] | "2023-03-19T02:01:35" | "2023-03-20T17:18:57" | null | NONE | null | false | {
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} | Similarly to how sequential chains connect chains in series, I thought we could have `ParallelChain`s connect chains in parallel. This is a useful abstraction when you want to independently process the same input with multiple different chains, possibly asynchronously (although I haven't added async yet).
This was largely inspired by the `ConcatenateChain` example [here](https://langchain.readthedocs.io/en/latest/modules/chains/getting_started.html#create-a-custom-chain-with-the-chain-class), although I thought that it would be good to have a generic class for handling multiple chains in parallel, to make nested composition easier. The larger intent is to compose chains similar to how `nn.Sequential` and `nn.Parallel` allowed for composing neural modules in series and in parallel in Torch7.
Here is an example (which is added to the docs)
```
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SimpleParallelChain
llm = OpenAI(temperature=0.9)
prompt_1 = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain_1 = LLMChain(llm=llm, prompt=prompt_1)
prompt_2 = PromptTemplate(
input_variables=["product"],
template="What is a good slogan for a company that makes {product}?",
)
chain_2 = LLMChain(llm=llm, prompt=prompt_2)
parallel_chain = SimpleParallelChain(
chains=[chain_1, chain_2],
input_variables=['product'],
output_variables=['name', 'slogan'],
verbose=True
)
output = parallel_chain("colorful socks")
print(f"Output:\n{output}")
```
which produces the output:
```
> Entering new SimpleParallelChain chain...
> Finished chain.
Output:
{'product': 'colorful socks', 'name': '\n\nLively Sockery.', 'slogan': '\n\n"Step Into Color with Our Socks!"'}
```
If having a class like this would be useful, there a few design choices I'd love to discuss:
1. Would it make more sense to have all the chains define their own input variables manually, or should we let the parallel chain define the input variables once and propagate that to all the child chains? An argument for the former would be that it follows the existing convention I've seen in defining chains so far. An argument for the latter would be that it would allow the user to define the input variables to the entire parallel block in one place (which would make the ParallelChain abstraction more powerful), without having to copy the same input variables for all child chains.
2. Any thoughts on whether it makes sense for this to be refactored in as a subcomponent of the MapReduceChain? The main reason for replacing the map component of the mapreduce by a ParallelChain would be that users can use ParallelChain for use cases other than applying the same llm for reducing a set of documents with the same prompt - they would instead be able to use multiple chains with different prompts each.
3. Any thoughts on how we would want to support multiple outputs for each child chain, i.e. how we would want to format the output of the ParallelChain? One possibility is to do a nested dict, like:
```
{
'output_for_chain_1: {'chain_1_output_1': str, 'chain_1_output_2': str, ... },
'output_for_chain_2: {'chain_2_output_1': str, 'chain_2_output_2': str, ... },
}
```
or flatten the keys with a delimiter, like:
```
{
'output_for_chain_1/chain_1_output_1': str, 'output_for_chain_1/chain_1_output_2': str, ... },
'output_for_chain_2/chain_2_output_1': str, 'output_for_chain_2/chain_2_output_2': str, ... },
}
``` | {
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https://api.github.com/repos/hwchase17/langchain/issues/1767 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1767/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1767/comments | https://api.github.com/repos/hwchase17/langchain/issues/1767/events | https://github.com/hwchase17/langchain/issues/1767 | 1,630,737,686 | I_kwDOIPDwls5hMxUW | 1,767 | openai.error.InvalidRequestError: This model's maximum context length is 4097 tokens | {
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"I got the same error message today. It suggested the following. \r\n\r\nInvalidRequestError: This model's maximum context length is 4097 tokens. However, you requested 4245 tokens (1745 in the messages, 2500 in the completion). Please reduce the length of the messages or completion. Clearly I will need a text request or response clipping approach to reduce seeing this error.",
"It was preceded by this warning - openai.py:608: UserWarning: You are trying to use a chat model. This way of initializing it **is no longer supported.** Instead, please use: `from langchain.chat_models import ChatOpenAI`\r\n warnings.warn(. _ I will check and see if calling using this new style performs better and report back"
] | "2023-03-19T01:53:30" | "2023-03-20T18:15:46" | null | NONE | null | null | null | When using the chat application, I encountered an error message stating "openai.error.InvalidRequestError: This model's maximum context length is 4097 tokens" when I asked a question like "Did he mention Stephen Breyer?".
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https://api.github.com/repos/hwchase17/langchain/issues/1766 | https://api.github.com/repos/hwchase17/langchain | https://api.github.com/repos/hwchase17/langchain/issues/1766/labels{/name} | https://api.github.com/repos/hwchase17/langchain/issues/1766/comments | https://api.github.com/repos/hwchase17/langchain/issues/1766/events | https://github.com/hwchase17/langchain/issues/1766 | 1,630,736,276 | I_kwDOIPDwls5hMw-U | 1,766 | Update poetry lock to allow SQLAlchemy v2 | {
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"It seems that jupyter-cache is another blocking dependency:\r\n\r\njupyter-cache\r\nhttps://github.com/hwchase17/langchain/blob/master/poetry.lock#L2526\r\nhttps://github.com/executablebooks/jupyter-cache/issues/92\r\nhttps://github.com/executablebooks/jupyter-cache/pull/93\r\n\r\nThis dependency was introduced in [pull request #534](https://github.com/hwchase17/langchain/pull/534/) but `jupyter_cache` doesn't seem to be called anywhere in the code base when you do `grep -r \"jupyter_cache\" .` in the root directory of the project.",
"`jupyter_cache` is a dependency for myst-nb which is used in creating the docs. [I do have a PR open with `jupyter-cache` to allow SQLAlchemy 2.](https://github.com/executablebooks/jupyter-cache/pull/93)",
"Thanks for the clarification and your efforts. Added some reactions and a \"+1\"."
] | "2023-03-19T01:48:23" | "2023-03-19T23:34:51" | null | NONE | null | null | null | It seems that #1578 adds support for SQLAlchemy v2 but the [poetry lock file](https://github.com/hwchase17/langchain/blob/8685d53adcdd0310e76349ecb4e2b87f980c4673/poetry.lock#L6211) is still at 1.4.46. | {
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} | [] | open | false | null | [] | null | [] | "2023-03-18T21:44:36" | "2023-03-18T21:44:36" | null | NONE | null | null | null | If I use langchain for question answering on a single document with any LLM, is there a way to also extract the corresponding character offsets (begin and end) of the response in the original document?
Thanks,
Ravi. | {
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