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PR_kwDOIPDwls5R4_zU

Create word.py

closed

NONE

Loader that loads word files, such as .doc or .docx file # Add a new DocumentLoader to support word file. <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ## @eyurtsev Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->

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I_kwDOIPDwls5neoa3

Query With Multiple Collections

open

NONE

Hi, I am using langchain to create collections in my local directory after that I am persisting it using below code from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter , TokenTextSplitter from langchain.llms import OpenAI from langchain.chat_models import ChatOpenAI from langchain.chains import VectorDBQA, RetrievalQA from langchain.document_loaders import TextLoader, UnstructuredFileLoader, DirectoryLoader loader = DirectoryLoader("D:/files/data") docs = loader.load() text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=200) texts = text_splitter.split_documents(docs) embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) vectordb = Chroma.from_documents(texts, embedding=embeddings, persist_directory = persist_directory, collection_name=my_collection) vectordb.persist() vectordb = None I am using above code for creating different different collection in the same persist_directory by just changing the collection name and the data files path, now lets say I have 5 collection in my persist directory my_collection1 my_collection2 my_collection3 my_collection4 my_collection5 Now If I want to perform querying to my data then I have to call my persist_directory with collection_name vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings, collection_name=my_collection3) qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(openai_api_key=openai_api_key), chain_type="stuff", retriever=vectordb.as_retriever(search_type="mmr"), return_source_documents=True) qa("query") so the issue is if I am using above code then I can perform only querying for my_collection3 but I want to perform querying to all my five collections, so can anyone please suggest, how can I do this or if it is not possible, I will be thankful to you. I had tried without collection name for ex- vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding) qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(openai_api_key=openai_api_key), chain_type="stuff", retriever=vectordb.as_retriever(search_type="mmr"), return_source_documents=True) qa("query") but in this case I am getting NoIndexException: Index not found, please create an instance before querying

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I_kwDOIPDwls5nebT7

ConversationalRetrievalChain is changing context of input

closed

NONE

### System Info The first time I query the LLM evertything is okay, the second time and in all the calls after that user query is totally changed. For example my input was "How are you today?" and the chain while trying to make this a standalone question gets confused and totally changes the question, file:///home/propsure/Pictures/Screenshots/Screenshot%20from%202023-06-01%2015-47-08.png This is how I am using the chain, ` QA_PROMPT = PromptTemplate(template=prompt_template, input_variables=["context","question", "chat_history" ]) chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=retriever, return_source_documents=False, verbose=True, max_tokens_limit=2048, combine_docs_chain_kwargs={'prompt': QA_PROMPT} ` ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Run any llm locally, I am using WizardVicuna (faced the same issue with OpenAI api), and try a conversationalretreival QA with chat_history. Also if I am doing something incorrectly please let me know much appreciated. ### Expected behavior there should be an option if we don't want it to condense the question, prompt. Because in context based QA we want a similar behaviour.

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I_kwDOIPDwls5neQZj

Self-querying with Chroma bug - Got invalid return object. Expected markdown code snippet with JSON object, but got ...

closed

NONE

### System Info ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ /opt/conda/lib/python3.10/site-packages/langchain/chains/query_constructor/base.py:36 in parse │ │ │ │ 33 │ def parse(self, text: str) -> StructuredQuery: │ │ 34 │ │ try: │ │ 35 │ │ │ expected_keys = ["query", "filter"] │ │ ❱ 36 │ │ │ parsed = parse_json_markdown(text, expected_keys) │ │ 37 │ │ │ if len(parsed["query"]) == 0: │ │ 38 │ │ │ │ parsed["query"] = " " │ │ 39 │ │ │ if parsed["filter"] == "NO_FILTER" or not parsed["filter"]: │ │ │ │ /opt/conda/lib/python3.10/site-packages/langchain/output_parsers/structured.py:27 in │ │ parse_json_markdown │ │ │ │ 24 │ │ 25 def parse_json_markdown(text: str, expected_keys: List[str]) -> Any: │ │ 26 │ if "```json" not in text: │ │ ❱ 27 │ │ raise OutputParserException( │ │ 28 │ │ │ f"Got invalid return object. Expected markdown code snippet with JSON " │ │ 29 │ │ │ f"object, but got:\n{text}" │ │ 30 │ │ ) │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ OutputParserException: Got invalid return object. Expected markdown code snippet with JSON object, but got: ```vbnet { "query": "chatbot refinement", "filter": "NO_FILTER" } ``` During handling of the above exception, another exception occurred: ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ /tmp/ipykernel_28206/2038672913.py:1 in <module> │ │ │ │ [Errno 2] No such file or directory: '/tmp/ipykernel_28206/2038672913.py' │ │ │ │ /opt/conda/lib/python3.10/site-packages/langchain/retrievers/self_query/base.py:73 in │ │ get_relevant_documents │ │ │ │ 70 │ │ """ │ │ 71 │ │ inputs = self.llm_chain.prep_inputs({"query": query}) │ │ 72 │ │ structured_query = cast( │ │ ❱ 73 │ │ │ StructuredQuery, self.llm_chain.predict_and_parse(callbacks=None, **inputs) │ │ 74 │ │ ) │ │ 75 │ │ if self.verbose: │ │ 76 │ │ │ print(structured_query) │ │ │ │ /opt/conda/lib/python3.10/site-packages/langchain/chains/llm.py:238 in predict_and_parse │ │ │ │ 235 │ │ """Call predict and then parse the results.""" │ │ 236 │ │ result = self.predict(callbacks=callbacks, **kwargs) │ │ 237 │ │ if self.prompt.output_parser is not None: │ │ ❱ 238 │ │ │ return self.prompt.output_parser.parse(result) │ │ 239 │ │ else: │ │ 240 │ │ │ return result │ │ 241 │ │ │ │ /opt/conda/lib/python3.10/site-packages/langchain/chains/query_constructor/base.py:49 in parse │ │ │ │ 46 │ │ │ │ limit=parsed.get("limit"), │ │ 47 │ │ │ ) │ │ 48 │ │ except Exception as e: │ │ ❱ 49 │ │ │ raise OutputParserException( │ │ 50 │ │ │ │ f"Parsing text\n{text}\n raised following error:\n{e}" │ │ 51 │ │ │ ) │ │ 52 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ OutputParserException: Parsing text ```vbnet { "query": "chatbot refinement", "filter": "NO_FILTER" } ``` raised following error: Got invalid return object. Expected markdown code snippet with JSON object, but got: ```vbnet { "query": "chatbot refinement", "filter": "NO_FILTER" } ``` ### 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 ``` class Dolly(LLM): history_data: Optional[List] = [] chatbot : Optional[hugchat.ChatBot] = None conversation : Optional[str] = "" #### WARNING : for each api call this library will create a new chat on chat.openai.com @property def _llm_type(self) -> str: return "custom" def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str: if stop is not None: pass #raise ValueError("stop kwargs are not permitted.") #token is a must check if self.chatbot is None: if self.conversation == "": self.chatbot = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") else: raise ValueError("Something went wrong") sleep(2) data = self.chatbot(prompt)[0]["generated_text"] #add to history self.history_data.append({"prompt":prompt,"response":data}) return data @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {"model": "DollyCHAT"} llm = Dolly() ``` Then I follow the instructions in https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/chroma_self_query.html and I got the above error, sometimes it works, but sometimes it doesn't. ### Expected behavior Should not return error and act like before (return the related documents)

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I_kwDOIPDwls5neBD2

Incorrect PromptTemplate memorizing

closed

CONTRIBUTOR

### System Info LangChain 0.0.187, Python 3.8.16, Linux Mint. ### Who can help? @hwchase17 @ago ### 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 - [X] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction This script demonstrates the issue: ```python from langchain.chains import ConversationChain from langchain.llms import FakeListLLM from langchain.memory import ConversationBufferMemory from langchain.prompts import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, ) sys_prompt = SystemMessagePromptTemplate.from_template("You are helpful assistant.") human_prompt = HumanMessagePromptTemplate.from_template("Hey, {input}") prompt = ChatPromptTemplate.from_messages( [ sys_prompt, MessagesPlaceholder(variable_name="history"), human_prompt, ] ) chain = ConversationChain( prompt=prompt, llm=FakeListLLM(responses=[f"+{x}" for x in range(10)]), memory=ConversationBufferMemory(return_messages=True, input_key="input"), verbose=True, ) chain({"input": "hi there!"}) chain({"input": "what's the weather?"}) ``` Output: ``` > Entering new ConversationChain chain... Prompt after formatting: System: You are helpful assistant. Human: Hey, hi there! > Finished chain. > Entering new ConversationChain chain... Prompt after formatting: System: You are helpful assistant. Human: hi there! <----- ISSUE AI: +0 Human: Hey, what's the weather? > Finished chain. ``` `ISSUE`: `Human` history message in the second request is incorrect. It provides raw input. ### Expected behavior Expected output: ``` > Entering new ConversationChain chain... Prompt after formatting: System: You are helpful assistant. Human: Hey, hi there! > Finished chain. > Entering new ConversationChain chain... Prompt after formatting: System: You are helpful assistant. Human: Hey, hi there! <----- EXPECTED AI: +0 Human: Hey, what's the weather? > Finished chain. ``` History message should contain rendered template string instead of raw input. As a workaround I add extra "rendering" step before the `ConversationChain`: ```python from langchain.chains import ConversationChain, SequentialChain, TransformChain from langchain.llms import FakeListLLM from langchain.memory import ConversationBufferMemory from langchain.prompts import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, PromptTemplate, SystemMessagePromptTemplate, ) # Extra step: pre-render user template. in_prompt = PromptTemplate.from_template("Hey, {input}") render_chain = TransformChain( input_variables=in_prompt.input_variables, output_variables=["text"], transform=lambda x: {"text": in_prompt.format(**x)}, ) prompt = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template("You are helpful assistant."), MessagesPlaceholder(variable_name="history"), HumanMessagePromptTemplate.from_template("{text}"), ] ) chat_chain = ConversationChain( prompt=prompt, llm=FakeListLLM(responses=(f"+{x}" for x in range(10))), memory=ConversationBufferMemory(return_messages=True, input_key="text"), input_key="text", verbose=True, ) chain = SequentialChain(chains=[render_chain, chat_chain], input_variables=["input"]) chain({"input": "hi there!"}) chain({"input": "what's the weather?"}) ``` Output: ``` > Entering new ConversationChain chain... Prompt after formatting: System: You are helpful assistant. Human: Hey, hi there! > Finished chain. > Entering new ConversationChain chain... Prompt after formatting: System: You are helpful assistant. Human: Hey, hi there! <--- FIXED AI: +0 Human: Hey, what's the weather? > Finished chain. ``` I have checked the code and looks like this behavior is by design: Both `Chain.prep_inputs()` and `Chain.prep_outputs()` pass only inputs/outputs to the `memory` so there is no way to store formatted/rendered template. Not sure if this is a design issue or incorrect `langchain` API usage. Docs say nothing about `PromptTemplate` restrictions, so I assumed it should work out of the box.

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PR_kwDOIPDwls5R4KX1

[Vectorstore] Added async gRPC methods to Qdrant vectorstore

closed

NONE

# Async gRPC methods to Qdrant vectorstore Changes: - Async methods to add: aadd_texts, asimilarity_search, asimilarity_search_with_score, amax_marginal_relevance_search_with_score - Helper methods to add: _document_from_scored_point_grpc, _document_from_scored_point_grpc, _build_condition_grpc, _qdrant_filter_from_dict_grpc In addition to rest, Qdrant supports the gRPC protocol, which allows calling asynchronous methods on the client. gRPC is an open source remote procedure call (RPC) framework developed by Google. gRPC uses a data serialization and RPC protocol called Protocol Buffers to enable efficient communication between clients and servers. By adding Qdrant's native asynchronous methods, we can solve the event loop blocking issue that occurs when using synchronous methods. Since these asynchronous methods only work on remote clients, not local, I've made it so that attempting to use them in a local environment (memory, disk) will result in a NotImplementedError. I was going to create a unit test functions, but I didn't add it for now because it requires a real Remote server to test the method, but it's working fine for now in my project.

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PR_kwDOIPDwls5R4BCt

Fixed incorrect prompt formatting for chat based ReAct agent

closed

NONE

# Fixed incorrect prompt formatting for chat based ReAct agent <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes #5390 Created two new classes (ReActStringPromptValue and ReActPromptTemplate) in order to handle correct prompt formatting for chat based models. My twitter handle: @gultepeoguz ## Before submitting Format/lint/tests already run. <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->

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I_kwDOIPDwls5ndg-X

Guanaco 65B model support

closed

NONE

### Feature request Please add support for the model Guanaco 65B, which is trained via qLoRA method. To be able to swap OpenAI model to Guanaco and perform same operations over it. ### Motivation The best performance and free model out there up to date 01.06.2023. ### Your contribution -

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PR_kwDOIPDwls5R34_A

Improve Error Messaging for APOC Procedure Failure in Neo4jGraph

closed

CONTRIBUTOR

## Improve Error Messaging for APOC Procedure Failure in Neo4jGraph This commit revises the error message provided when the 'apoc.meta.data()' procedure fails. Previously, the message simply instructed the user to install the APOC plugin in Neo4j. The new error message is more specific. Also removed an unnecessary newline in the Cypher statement variable: `node_properties_query`. Fixes #5545 ## Who can review? - @vowelparrot - @dev2049

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I_kwDOIPDwls5ndSQV

Issue: Improve Error Messaging When APOC Procedures Fail in Neo4jGraph

closed

CONTRIBUTOR

### Issue you'd like to raise. In the current implementation, when an APOC procedure fails, a generic error message is raised stating: "Could not use APOC procedures. Please install the APOC plugin in Neo4j." This message can lead to user confusion as it suggests the APOC plugin is not installed when in reality it may be installed but not correctly configured or permitted to run certain procedures. This issue is encountered specifically when the refresh_schema function calls apoc.meta.data(). The function apoc.meta.data() isn't allowed to run under default configurations in the Neo4j database, thus leading to the mentioned error message. Here is the code snippet where the issue arises: ``` # Set schema try: self.refresh_schema() except neo4j.exceptions.ClientError raise ValueError( "Could not use APOC procedures. " "Please install the APOC plugin in Neo4j." ) ``` ### Suggestion: To improve the user experience, I propose that the error message should be made more specific. Instead of merely advising users to install the APOC plugin, it would be beneficial to indicate that certain procedures may not be configured or whitelisted to run by default and to guide the users to check their configurations. I believe this will save users time when troubleshooting and will reduce the potential for confusion.

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I_kwDOIPDwls5nc9Jj

The same code, sometimes throwing an exception(ValueError: Could not parse output), sometimes running correctly

closed

NONE

### Issue you'd like to raise. ``` loaders = [TextLoader("13.txt"), TextLoader("14.txt"), TextLoader("15.txt"),TextLoader("16.txt"), TextLoader("17.txt"), TextLoader("18.txt")] documents = [] for loader in loaders: documents.extend(loader.load()) text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=150) documents = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(documents, embeddings) qa = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, chain_type="map_rerank", retriever=vectorstore.as_retriever(), return_source_documents=True) query = "茶艺师报名条件是什么，使用中文回答" chat_history = [] result = qa({'question': query}) ``` sometimes this code raise ValueError: Could not parse output，but when I rerun result = qa({'question': query}), this may return the anwser. how can I fix this? ### Suggestion: I wonder why this happens, and how to fix it. please help me!!

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PR_kwDOIPDwls5R3Pdy

Incorrect argument count handling

closed

CONTRIBUTOR

Throwing ToolException when incorrect arguments are passed to tools so that that agent can course correct them. # Incorrect argument count handling I was facing an error where the agent passed incorrect arguments to tools. As per the discussions going around, I started throwing ToolException to allow the model to course correct. ## Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->

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I_kwDOIPDwls5ncv5p

Issue: previous message condensing time on `ConversationalRetrievalChain`

closed

NONE

### Issue you'd like to raise. I got a scenario where I'm using the `ConversationalRetrievalChain` with chat history. The problem is, it was streaming the condensed output of the question. Not the actual answer. So I separated the models, one for condensing the question and one for answering with streaming. But as I suspected, the condensing chain is eating up a time to generate the condensed output of the question, and the actual streaming of the answer is waiting for the condensed question generator. some of my implementations: ```python callback = AsyncIteratorCallbackHandler() q_generator_llm = ChatOpenAI( openai_api_key=settings.openai_api_key, ) streaming_llm = ChatOpenAI( openai_api_key=settings.openai_api_key, streaming=True, callbacks=[callback], ) question_generator = LLMChain(llm=q_generator_llm, prompt=CONDENSE_QUESTION_PROMPT) doc_chain = load_qa_chain(llm=streaming_llm, chain_type="stuff", prompt=prompt) qa_chain = ConversationalRetrievalChain( retriever=collection_store.as_retriever(search_kwargs={"k": 3}), combine_docs_chain=doc_chain, question_generator=question_generator, return_source_documents=True, ) history = [] task = asyncio.create_task( qa_chain.acall({ "question": q, "chat_history": history }), ) ``` ___ ### **Any workaround on how I avoid condensing the question to save time? Or any efficient way to resolve the issue?** ### Suggestion: _No response_

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https://api.github.com/repos/langchain-ai/langchain/issues/5541

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https://github.com/langchain-ai/langchain/pull/5541

PR_kwDOIPDwls5R3GrD

Cypher search: Check if generated Cypher is provided in backticks

closed

CONTRIBUTOR

# Check if generated Cypher code is wrapped in backticks Some LLMs like the VertexAI like to explain how they generated the Cypher statement and wrap the actual code in three backticks:  I have observed a similar pattern with OpenAI chat models in a conversational settings, where multiple user and assistant message are provided to the LLM to generate Cypher statements, where then the LLM wants to maybe apologize for previous steps or explain its thoughts. Interestingly, both OpenAI and VertexAI wrap the code in three backticks if they are doing any explaining or apologizing. Checking if the generated cypher is wrapped in backticks seems like a low-hanging fruit to expand the cypher search to other LLMs and conversational settings.

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https://github.com/langchain-ai/langchain/pull/5540

PR_kwDOIPDwls5R3DqJ

Harrison/pipeline prompt

closed

COLLABORATOR

idea is to make prompts more composable

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https://api.github.com/repos/langchain-ai/langchain/issues/5539

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I_kwDOIPDwls5ncUxG

Allow users to pass local embeddings to Weaviate Hybrid Search Retriever

closed

NONE

### Feature request Fork off from this issue: https://github.com/hwchase17/langchain/issues/5300 The idea is to provide the WeaviateHybridSearchRetriever with the ability to use local embeddings similar to the Weaviate vectorstore. Specifically, for the `WeaviateHybridSearchRetriever.add_documents()` and `WeaviateHybridSearchRetriever.get_relevant_documents()` functions to work similar to the `Weviate.from_texts()` function where there is the option to use local embeddings if passed during creation. Additionally, the `WeaviateHybridSearchRetriever._create_schema_if_missing()` function likely needs to remove the default addition of a vectorizer in the schema object (related issue here: https://github.com/hwchase17/langchain/issues/5300). ### Motivation This will allow those of us running Weaviate without embedding modules (like myself) to use the Weaviate Hybrid Search Retriever. ### Your contribution I am planning on working to get a fix locally, I can potentially submit this as a PR down the line. Busy this week so others would probably beat me to it. I can review though.

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https://api.github.com/repos/langchain-ai/langchain/issues/5538

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PR_kwDOIPDwls5R2xSx

add brave search util

closed

COLLABORATOR

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https://api.github.com/repos/langchain-ai/langchain/issues/5537

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I_kwDOIPDwls5ncFRU

OpenAI and Azure OpenAI - calls one after another

closed

NONE

### System Info langchain - 0.0.174 / 0.0.178 / 0.0.187 python3 ### Who can help? @hwchase17 @agola11 ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. Call OpenAI setting the parameters - openai_api_type , openai_api_version , openai_api_base , openai_api_key -- Successful OpenAI request response 2. Call Azure OpenAI setting the parameters - openai_api_type , openai_api_version , openai_api_base , openai_api_key -- Fails All subsequent calls fails Alternatively, if you first call Azure OpenAI with parameters set correctly - that succeeds, but OpenAI fails. And all subsequent fails. Each independently works - so guessing the. parameter values work as expected. But when one after other is called, the second API (OpenAI or Azure OpenAI - which ever is called second) - fails ### Expected behavior If parameters are set correctly, both should work as required. If independently they work if app is restarted, why would it fail if they are called sequentiially?

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https://api.github.com/repos/langchain-ai/langchain/issues/5536

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I_kwDOIPDwls5ncEzk

`RetrievalQAWithSourcesChain` not returning sources in `sources` field.

closed

CONTRIBUTOR

### System Info System Info (Docker Dev Container): ``` PRETTY_NAME="Debian GNU/Linux 10 (buster)" NAME="Debian GNU/Linux" VERSION_ID="10" VERSION="10 (buster)" VERSION_CODENAME=buster ID=debian HOME_URL="https://www.debian.org/" SUPPORT_URL="https://www.debian.org/support" BUG_REPORT_URL="https://bugs.debian.org/" ``` Python: 3.10 Pip: ``` absl-py 1.4.0 aiohttp 3.8.4 aiosignal 1.3.1 antlr4-python3-runtime 4.9.3 anyio 3.6.2 argilla 1.6.0 async-timeout 4.0.2 attrs 23.1.0 backoff 2.2.1 cachetools 5.3.0 certifi 2022.12.7 cffi 1.15.1 charset-normalizer 3.1.0 click 8.1.3 cloudpickle 2.2.1 cmake 3.26.3 coloredlogs 15.0.1 commonmark 0.9.1 contourpy 1.0.7 cryptography 40.0.2 cycler 0.11.0 dataclasses-json 0.5.7 Deprecated 1.2.13 detectron2 0.4 effdet 0.3.0 et-xmlfile 1.1.0 exceptiongroup 1.1.1 fastapi 0.95.1 filelock 3.11.0 flatbuffers 23.3.3 fonttools 4.39.3 frozenlist 1.3.3 future 0.18.3 fvcore 0.1.3.post20210317 google-auth 2.17.3 google-auth-oauthlib 1.0.0 gptcache 0.1.11 greenlet 2.0.2 grpcio 1.53.0 h11 0.14.0 httpcore 0.16.3 httpx 0.23.3 huggingface-hub 0.13.4 humanfriendly 10.0 idna 3.4 iniconfig 2.0.0 iopath 0.1.10 Jinja2 3.1.2 joblib 1.2.0 kiwisolver 1.4.4 langchain 0.0.141 layoutparser 0.3.4 lit 16.0.1 lxml 4.9.2 Markdown 3.4.3 MarkupSafe 2.1.2 marshmallow 3.19.0 marshmallow-enum 1.5.1 matplotlib 3.7.1 monotonic 1.6 mpmath 1.3.0 msg-parser 1.2.0 multidict 6.0.4 mypy-extensions 1.0.0 networkx 3.1 nltk 3.8.1 numpy 1.23.5 nvidia-cublas-cu11 11.10.3.66 nvidia-cuda-cupti-cu11 11.7.101 nvidia-cuda-nvrtc-cu11 11.7.99 nvidia-cuda-runtime-cu11 11.7.99 nvidia-cudnn-cu11 8.5.0.96 nvidia-cufft-cu11 10.9.0.58 nvidia-curand-cu11 10.2.10.91 nvidia-cusolver-cu11 11.4.0.1 nvidia-cusparse-cu11 11.7.4.91 nvidia-nccl-cu11 2.14.3 nvidia-nvtx-cu11 11.7.91 oauthlib 3.2.2 olefile 0.46 omegaconf 2.3.0 onnxruntime 1.14.1 openai 0.27.4 openapi-schema-pydantic 1.2.4 opencv-python 4.6.0.66 openpyxl 3.1.2 packaging 23.1 pandas 1.5.3 pdf2image 1.16.3 pdfminer.six 20221105 pdfplumber 0.9.0 pgvector 0.1.6 Pillow 9.5.0 pip 23.1 pluggy 1.0.0 portalocker 2.7.0 protobuf 4.22.3 psycopg2-binary 2.9.6 pyasn1 0.4.8 pyasn1-modules 0.2.8 pycocotools 2.0.6 pycparser 2.21 pydantic 1.10.7 pydot 1.4.2 Pygments 2.15.0 pypandoc 1.11 pyparsing 3.0.9 pypdf 3.9.0 pytesseract 0.3.10 pytest 7.3.1 python-dateutil 2.8.2 python-docx 0.8.11 python-dotenv 1.0.0 python-magic 0.4.27 python-multipart 0.0.6 python-poppler 0.4.0 python-pptx 0.6.21 pytz 2023.3 PyYAML 6.0 regex 2023.3.23 requests 2.28.2 requests-oauthlib 1.3.1 rfc3986 1.5.0 rich 13.0.1 rsa 4.9 scipy 1.10.1 setuptools 65.5.1 six 1.16.0 sniffio 1.3.0 SQLAlchemy 1.4.47 starlette 0.26.1 sympy 1.11.1 tabulate 0.9.0 tenacity 8.2.2 tensorboard 2.12.2 tensorboard-data-server 0.7.0 tensorboard-plugin-wit 1.8.1 termcolor 2.2.0 tiktoken 0.3.3 timm 0.6.13 tokenizers 0.13.3 tomli 2.0.1 torch 2.0.0 torchaudio 2.0.1 torchvision 0.15.1 tqdm 4.65.0 transformers 4.28.1 triton 2.0.0 typing_extensions 4.5.0 typing-inspect 0.8.0 unstructured 0.5.12 unstructured-inference 0.3.2 urllib3 1.26.15 uvicorn 0.21.1 Wand 0.6.11 Werkzeug 2.2.3 wheel 0.40.0 wrapt 1.14.1 XlsxWriter 3.1.0 yacs 0.1.8 yarl 1.8.2 ``` ### Who can help? @hwchase17 ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Write the code below: ``` chain = RetrievalQAWithSourcesChain.from_chain_type( llm=ChatOpenAI(openai_api_key=api_key), chain_type="map_reduce", retriever=retriever, ) llm_call = "random llm call" result = chain({ "question": llm_call, }, return_only_outputs=True ) ``` ### Expected behavior I'm expecting that I'll be having a `result["answer"]` and non empty `result["sources"]` but here's what I get instead:  As you can see, `sources` is empty but it's included in `result["answer"]` as a string.

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https://api.github.com/repos/langchain-ai/langchain/issues/5535

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I_kwDOIPDwls5nb5kT

Add Tigris vectorstore for vector search

closed

CONTRIBUTOR

### Feature request Support Tigris as a vector search backend ### Motivation Tigris is a Serverless NoSQL Database and Search Platform and have their [vector search](https://www.tigrisdata.com/docs/concepts/vector-search/python/) product. It will be great option for users to use an integrated database and search product. ### Your contribution I can submit a a PR

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https://api.github.com/repos/langchain-ai/langchain/issues/5533

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PR_kwDOIPDwls5R2KbU

add maxcompute

closed

CONTRIBUTOR

cc @pengwork (fresh branch, no creds)

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https://api.github.com/repos/langchain-ai/langchain/issues/5532

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I_kwDOIPDwls5nbaQF

Issue: how stream results with long context

open

NONE

### Issue you'd like to raise. i follow the chapter “Chat Over Documents with Chat History” to build a bot chat with pdf， i want to streanming return, but when i use stuff chain like this ```python doc_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff", prompt=QA_PROMPT) chain = ConversationalRetrievalChain( retriever=vector_db.as_retriever(), question_generator=question_generator, combine_docs_chain=doc_chain ) ``` it return "This model's maximum context length is 4097 tokens, however you requested 5741 tokens (5485 in your prompt; 256 for the completion). Please reduce your prompt; or completion length" when i use map_reduce chain ```python doc_chain = load_qa_chain(OpenAI(temperature=0,streaming=True,callbacks=[StreamingStdOutCallbackHandler()]), chain_type="map_reduce", combine_prompt=getQaMap_reducePromot()) ``` it return "Cannot stream results with multiple prompts." how to resolve it when the context is too long ### Suggestion: _No response_

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https://api.github.com/repos/langchain-ai/langchain/issues/5531

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https://github.com/langchain-ai/langchain/issues/5531

I_kwDOIPDwls5na7oo

Sampling parameters are ignored by vertexai

closed

CONTRIBUTOR

start_chat() constructs a vertextai _ChatSession and sets class variables with the parameters, but send_message() will not use those parameters if send_message is called w/o parameters. This is because send_message() has default values for the parameters which are set to global variables. You can fix this by moving **self._default_params to the send_message() call. https://github.com/hwchase17/langchain/blob/359fb8fa3ae0b0904dbb36f998cd2339ea0aec0f/langchain/chat_models/vertexai.py#LL122C75-L122C75

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https://api.github.com/repos/langchain-ai/langchain/issues/5530

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I_kwDOIPDwls5na4rk

Remove Chat Models

closed

NONE

### Feature request Chat models relying on `SystemMessage`, ... instead of simple text, hinder creating longer prompts. It would have been much simpler to avoid special casing chat models, and instead parse special tokens in the text prompt to separate system, human, ai, ... ### Motivation Something similar to [this](https://github.com/microsoft/MM-REACT/blob/main/langchain/llms/openai.py#L211) that uses `<|im_start|>system\nsystem message<|im_end|>` would make it easier to keep the same code for models, and just use different prompts for chat endpints. For example, it is perfectly valid to have 2 system messages, and I found it improves the results to have a system message at the beginning, and [one after](https://github.com/microsoft/MM-REACT/blob/main/langchain/agents/assistant/prompt.py#L191) some zero-shot examples right before the input. ### Your contribution I can send the PR if there is any interest.

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https://api.github.com/repos/langchain-ai/langchain/issues/5529

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PR_kwDOIPDwls5R1exn

[retrievers][knn] Replace loop appends with list comprehension.

closed

CONTRIBUTOR

# Replace loop appends with list comprehension. It's much faster, more idiomatic and slightly more readable.

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https://api.github.com/repos/langchain-ai/langchain/issues/5528

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https://github.com/langchain-ai/langchain/pull/5528

PR_kwDOIPDwls5R1dsG

Replace loop appends with list comprehension.

closed

CONTRIBUTOR

# Replace loop appends with list comprehension. It's significantly faster because it avoids repeated method lookup. It's also more idiomatic and readable.

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https://api.github.com/repos/langchain-ai/langchain/issues/5527

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PR_kwDOIPDwls5R1aK1

Replace enumerate with zip.

closed

CONTRIBUTOR

# Replace enumerate with zip. It's more idiomatic and slightly more readable.

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https://api.github.com/repos/langchain-ai/langchain/issues/5526

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https://github.com/langchain-ai/langchain/pull/5526

PR_kwDOIPDwls5R1ZWN

Replace list comprehension with generator.

closed

CONTRIBUTOR

# Replace list comprehension with generator. Since these strings can be fairly long, it's best to not construct unnecessary temporary list just to pass it to `join`. Generators produce items one-by-one and even though they are slightly more expensive than lists in terms of CPU they are much more memory-friendly and slightly more readable.

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https://api.github.com/repos/langchain-ai/langchain/issues/5525

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https://github.com/langchain-ai/langchain/pull/5525

PR_kwDOIPDwls5R1WZw

Add minor fixes for PySpark Document Loader Docs

closed

CONTRIBUTOR

# Add minor fixes for PySpark Document Loader Docs Renamed "PySpack" to "PySpark" and executed the notebook to show outputs.

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https://api.github.com/repos/langchain-ai/langchain/issues/5524

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https://github.com/langchain-ai/langchain/pull/5524

PR_kwDOIPDwls5R1GN7

docs: unstructured no longer requires installing detectron2 from source

closed

CONTRIBUTOR

# Update Unstructured docs to remove the `detectron2` install instructions Removes `detectron2` installation instructions from the Unstructured docs because installing `detectron2` is no longer required for `unstructured>=0.7.0`. The `detectron2` model now runs using the ONNX runtime. ## Who can review? @hwchase17 @eyurtsev

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https://github.com/langchain-ai/langchain/pull/5523

PR_kwDOIPDwls5R1Fnq

Skips creating boto client for Bedrock if passed in constructor

closed

CONTRIBUTOR

# Skips creating boto client if passed in constructor Current LLM and Embeddings class always creates a new boto client, even if one is passed in a constructor. This blocks certain users from passing in externally created boto clients, for example in SSO authentication. ## Who can review? @hwchase17 @jasondotparse @rsgrewal-aws <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->

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https://github.com/langchain-ai/langchain/issues/5522

I_kwDOIPDwls5naUj-

Query execution with langchain LLM pipeline is happening on CPU, even if model is loaded on GPU

closed

NONE

Hi, I am building a chatbot using LLM like fastchat-t5-3b-v1.0 and want to reduce my inference time. I am loading the entire model on GPU, using device_map parameter, and making use of `langchain.llms.HuggingFacePipeline` agent for querying the LLM model. Also specifying the device=0 ( which is the 1st rank GPU) for hugging face pipeline as well. I am monitoring the GPU and CPU usage throughout the entire execution, and I can see that though my model is on GPU, at the time of querying the model, it makes use of CPU. The spike in CPU usage shows that query execution is happening on CPU. Below is the code that I am using to do inference on Fastchat LLM. ``` from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex, PromptHelper, LLMPredictor from langchain.embeddings.huggingface import HuggingFaceEmbeddings from llama_index import LangchainEmbedding, ServiceContext from transformers import T5Tokenizer, T5ForConditionalGeneration from accelerate import init_empty_weights, infer_auto_device_map model_name = 'lmsys/fastchat-t5-3b-v1.0' config = T5Config.from_pretrained(model_name ) with init_empty_weights(): model_layer = T5ForConditionalGeneration(config=config) device_map = infer_auto_device_map(model_layer, max_memory={0: "12GiB",1: "12GiB", "cpu": "0GiB"}, no_split_module_classes=["T5Block"]) # the value for is : device_map = {'': 0}. i.e loading model in 1st GPU model = T5ForConditionalGeneration.from_pretrained(model_name, torch_dtype=torch.float16, device_map=device_map, offload_folder="offload", offload_state_dict=True) tokenizer = T5Tokenizer.from_pretrained(model_name) from transformers import pipeline pipe = pipeline( "text2text-generation", model=model, tokenizer=tokenizer, device= 0, max_length=1536, temperature=0, top_p = 1, num_beams=1, early_stopping=False ) from langchain.llms import HuggingFacePipeline llm = HuggingFacePipeline(pipeline=pipe) embed_model = LangchainEmbedding(HuggingFaceEmbeddings()) # set maximum input size max_input_size = 2048 # set number of output tokens num_outputs = 512 # set maximum chunk overlap max_chunk_overlap = 20 # set chunk size limit chunk_size_limit = 300 prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap) service_context = ServiceContext.from_defaults(embed_model=embed_model, llm_predictor=LLMPredictor(llm), prompt_helper=prompt_helper, chunk_size_limit=chunk_size_limit) # build index documents = SimpleDirectoryReader('data').load_data() new_index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context) query_engine = new_index.as_query_engine( response_mode='no_text', verbose=True, similarity_top_k=2 ) template = """ A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. ### Human: Given the context: --- {context} --- Answer the following question: --- {input} ### Assistant: """ from langchain import LLMChain, PromptTemplate prompt = PromptTemplate( input_variables=["context", "input"], template=template, ) chain = LLMChain( llm=llm, prompt=prompt, verbose=True ) user_input= "sample query question?" context = query_engine.query(user_input) concatenated_context = ' '.join(map(str, [node.node.text for node in context.source_nodes])) response = chain.run({"context": concatenated_context, "input": user_input}) ``` Here the “data” folder has my full input text in pdf format, and am using the GPTVectoreStoreIndex and hugging face pipeline to build the index on that and fetch the relevant chunk to generate the prompt with context and user_input Then using LLMChain agent from langchain library to generate the response from FastChat model as shown in the code. Please have a look, and let me know if this is the expected behaviour. how can I make use of GPU for query execution as well? to reduce the inference response time.

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https://api.github.com/repos/langchain-ai/langchain/issues/5521

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I_kwDOIPDwls5nZ2cN

Tracing V2 doesn't work

closed

NONE

### System Info ``` $ langchain env LangChain Environment: library_version:0.0.184 platform:Linux-5.4.0-146-generic-x86_64-with-glibc2.31 runtime:python runtime_version:3.11.3 ``` ### Who can help? _No response_ ### Information - [x] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction Following the [tracing v2 instructions](https://python.langchain.com/en/latest/tracing/agent_with_tracing.html#beta-tracing-v2), run: ``` $ langchain plus start WARN[0000] The "OPENAI_API_KEY" variable is not set. Defaulting to a blank string. [+] Running 2/2 ⠿ langchain-frontend Pulled 5.3s ⠿ langchain-backend Pulled 9.5s unable to prepare context: path "frontend-react/." not found langchain plus server is running at http://localhost. To connect locally, set the following environment variable when running your LangChain application. LANGCHAIN_TRACING_V2=true ``` It looks like neither the `frontend-react` or `backend` folders referenced by the [`docker-compose.yaml`](https://github.com/hwchase17/langchain/blob/f72bb966f894f99c9ffc2c730be392c71d020ac8/langchain/cli/docker-compose.yaml#L14) are in the repository, thus docker won't build them. Maybe we should remove the `build:` section of the YAML when deploying to users so they simply pull the images from the Docker Hub. ### Expected behavior It should start properly.

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https://api.github.com/repos/langchain-ai/langchain/issues/5519

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https://github.com/langchain-ai/langchain/issues/5519

I_kwDOIPDwls5nZemU

Chroma: Constructor takes wrong embedding function (document vs query)

closed

NONE

### System Info Most recent version of Langchain Python: 3.10.8 MacOS 13.4 - M1 ### Who can help? @hwchase17 @agola11 ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction The Chroma constructor in the vectorstore section uses the document function when it should be the query function for embeddings. As a result, if the documents parameter is blank when using Chroma, Langchain will error out with a ValidationError. Please change line 95 to be embed_query instead of embed_documents [here](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/chroma.py) in order for this to work / be consistent with the rest of the vectorstore wrappers ### Expected behavior Use the query function instead of the documents function for use with embeddings in Chroma

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https://github.com/langchain-ai/langchain/pull/5518

PR_kwDOIPDwls5R0Kvs

added DeepLearing.AI course link

closed

COLLABORATOR

# added DeepLearing.AI course link ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: not @hwchase17 - hehe

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PR_kwDOIPDwls5R0Cw_

Update Tracer Auth / Reduce Num Calls

closed

CONTRIBUTOR

Update the session creation and calls

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https://github.com/langchain-ai/langchain/pull/5516

PR_kwDOIPDwls5Rz3Aw

Change methodology for scraping

closed

CONTRIBUTOR

It seems that the ReadTheDocsLoader is trying to parse and clean HTML contents from specific tags in the HTML file. If the HTML file doesn't contain the exact tag, the page_content will be empty. The loader is looking for the "main" tag with the id "main-content" and if it doesn't find it, it's looking for a "div" tag with the role "main". If neither is found, it returns an empty string. One way to fix this issue is to adjust the tags to those present in the HTML files to be scraped. @eyurtsev

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https://github.com/langchain-ai/langchain/pull/5515

PR_kwDOIPDwls5Rz2kY

Fix: Qdrant ids

closed

CONTRIBUTOR

# Fix Qdrant ids creation There has been a bug in how the ids were created in the Qdrant vector store. They were previously calculated based on the texts. However, there are some scenarios in which two documents may have the same piece of text but different metadata, and that's a valid case. Deduplication should be done outside of insertion. It has been fixed and covered with the integration tests. ## Who can review? @dev2049 @hwchase17

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https://github.com/langchain-ai/langchain/pull/5514

PR_kwDOIPDwls5Rz0y-

Reference OpenAIEmbeddings client attr as private

closed

CONTRIBUTOR

Reference OpenAIEmbeddings client attr as private When utilising Pylance it is currently unable to manage interpretation of the `client` attribute within `OpenAIEmbeddings` as private. This results in the following errors being displayed:  Additionally newer versions of `mypy` also identify this issue: `tests/integration_tests/embeddings/test_openai.py:11: error: Missing named argument "client" for "OpenAIEmbeddings" [call-arg]` One minor note is that I've include a property setter to preserve any access patterns for `OpenAIEmbeddings.client` /cc @hwchase17 - project lead /cc @agola11 - Models

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I_kwDOIPDwls5nY48T

Flan-t5-xxl doesnot work with openapi_agent

closed

NONE

### Issue you'd like to raise. I have created a pipeline. And want to use the same pipeline in openapi_agent. When I run the following command: ibm_agent = planner.create_openapi_agent(ibm_api_spec, requests_wrapper, hf_pipeline) I get error out of memory error. I'm using flan-t5-xxl llm, which consumes 22GB of memory. I have 18GB left. ``` tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xxl") model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-xxl", torch_dtype=torch.float16, device_map="auto") instruct_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer, pad_token_id=tokenizer.eos_token_id, torch_dtype=torch.bfloat16, device='cuda:0', max_length=2000) hf_pipeline = HuggingFacePipeline(pipeline=instruct_pipeline) agent = planner.create_openapi_agent(api_spec, requests_wrapper, hf_pipeline) user_query = "query" agent.run(user_query) ``` When i run code i get out following error ``` > Entering new AgentExecutor chain... Action: api_planner Action Input: api_planner(query) api_planner(query) api_controller(api_planner(query)) Traceback (most recent call last): File "/home/kiran/dolly/agents.py", line 79, in <module> ibm_agent.run(user_query) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 236, in run return self(args[0], callbacks=callbacks)[self.output_keys[0]] File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 140, in __call__ raise e File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 134, in __call__ self._call(inputs, run_manager=run_manager) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 953, in _call next_step_output = self._take_next_step( File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 820, in _take_next_step observation = tool.run( File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/tools/base.py", line 294, in run raise e File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/tools/base.py", line 266, in run self._run(*tool_args, run_manager=run_manager, **tool_kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/tools/base.py", line 409, in _run self.func( File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 236, in run return self(args[0], callbacks=callbacks)[self.output_keys[0]] File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 140, in __call__ raise e File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 134, in __call__ self._call(inputs, run_manager=run_manager) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/chains/llm.py", line 69, in _call response = self.generate([inputs], run_manager=run_manager) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/chains/llm.py", line 79, in generate return self.llm.generate_prompt( File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/llms/base.py", line 134, in generate_prompt return self.generate(prompt_strings, stop=stop, callbacks=callbacks) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/llms/base.py", line 191, in generate raise e File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/llms/base.py", line 185, in generate self._generate(prompts, stop=stop, run_manager=run_manager) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/llms/base.py", line 436, in _generate self._call(prompt, stop=stop, run_manager=run_manager) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/langchain/llms/huggingface_pipeline.py", line 168, in _call response = self.pipeline(prompt) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/pipelines/text2text_generation.py", line 165, in __call__ result = super().__call__(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1119, in __call__ return self.run_single(inputs, preprocess_params, forward_params, postprocess_params) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1126, in run_single model_outputs = self.forward(model_inputs, **forward_params) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1025, in forward model_outputs = self._forward(model_inputs, **forward_params) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/pipelines/text2text_generation.py", line 187, in _forward output_ids = self.model.generate(**model_inputs, **generate_kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/generation/utils.py", line 1322, in generate model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation( File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/generation/utils.py", line 638, in _prepare_encoder_decoder_kwargs_for_generation model_kwargs["encoder_outputs"]: ModelOutput = encoder(**encoder_kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward output = old_forward(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1086, in forward layer_outputs = layer_module( File "/home/kiran/dolly/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward output = old_forward(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 693, in forward self_attention_outputs = self.layer[0]( File "/home/kiran/dolly/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward output = old_forward(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 600, in forward attention_output = self.SelfAttention( File "/home/kiran/dolly/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward output = old_forward(*args, **kwargs) File "/home/kiran/dolly/venv/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 530, in forward scores = torch.matmul( torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 28.28 GiB (GPU 0; 39.43 GiB total capacity; 25.09 GiB already allocated; 13.13 GiB free; 25.12 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF ```

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https://api.github.com/repos/langchain-ai/langchain/issues/5512

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https://github.com/langchain-ai/langchain/pull/5512

PR_kwDOIPDwls5Rzr9Y

add allow_download as class attribute for GPT4All

closed

CONTRIBUTOR

# Added support for download GPT4All model if does not exist I've include the class attribute `allow_download` to the GPT4All class. By default, `allow_download` is set to False. ## Changes Made - Added a new class attribute, `allow_download`, to the GPT4All class. - Updated the `validate_environment` method to pass the `allow_download` parameter to the GPT4All model constructor. ## Context This change provides more control over model downloading in the GPT4All class. Previously, if the model file was not found in the cache directory `~/.cache/gpt4all/`, the package returned error "Failed to retrieve model (type=value_error)". Now, if `allow_download` is set as True then it will use GPT4All package to download it . With the addition of the `allow_download` attribute, users can now choose whether the wrapper is allowed to download the model or not. ## Dependencies There are no new dependencies introduced by this change. It only utilizes existing functionality provided by the GPT4All package. ## Testing Since this is a minor change to the existing behavior, the existing test suite for the GPT4All package should cover this scenario ## Reviewers - @hwchase17 - @agola11

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I_kwDOIPDwls5nYvzo

Automatically detect input_variables from PromptTemplate string

closed

NONE

### Feature request If input_variables is not passed, try to detect them automatically as those which are surrounded by curly braces: E.g. ``` prompt_template = PromptTemplate(template="What is the price of {product_name}?") ## Automatically detects the input_variables to be ['product_name'] ``` ### Motivation This has been bugging me for a while and makes it more cumbersome. ### Your contribution You can use the code mentioned below, it's literally that simple (at least for f-strings). I can submit a PR. ``` def str_format_args(x: str, named_only: bool = True) -> List[str]: ## Ref: https://stackoverflow.com/a/46161774/4900327 args: List[str] = [ str(tup[1]) for tup in string.Formatter().parse(x) if tup[1] is not None ] if named_only: args: List[str] = [ arg for arg in args if not arg.isdigit() and len(arg) > 0 ] return args str_format_args("What is the price of {product_name}?") ## Returns ['product_name'] ```

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I_kwDOIPDwls5nYpqG

ConversationalRetrievalChain new_question only from the question_generator only for retrieval and not for combine_docs_chain

closed

CONTRIBUTOR

### Discussed in https://github.com/hwchase17/langchain/discussions/5499 <div type='discussions-op-text'> ^{Originally posted by **lucasiscovici** May 31, 2023} Hello and thank you for this amazing library. Here we : - get question - get new_question with the question_generator - retrieve docs with _get_docs and the new_question - call the combine_docs_chain with the new_question and the docs 1/ It's possible to allow to call the question_generator event if the chat_history_str is empty ? i have to transform the question to search query to call the search engine even if the chat history is empty 2/ It's possible to not use the new_question in the combine_docs_chain call ? i need the true question and not the new question (the search query) to call the llm for the qa Thanks in advance ```python def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, Any]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() question = inputs["question"] get_chat_history = self.get_chat_history or _get_chat_history chat_history_str = get_chat_history(inputs["chat_history"]) if chat_history_str: callbacks = _run_manager.get_child() new_question = self.question_generator.run( question=question, chat_history=chat_history_str, callbacks=callbacks ) else: new_question = question docs = self._get_docs(new_question, inputs) new_inputs = inputs.copy() new_inputs["question"] = new_question new_inputs["chat_history"] = chat_history_str answer = self.combine_docs_chain.run( input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs ) if self.return_source_documents: return {self.output_key: answer, "source_documents": docs} else: return {self.output_key: answer} ```

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PR_kwDOIPDwls5Rzh01

Add Managed Motorhead

closed

CONTRIBUTOR

# Add Managed Motorhead This change enabled MotorheadMemory to utilize Metal's managed version of Motorhead. We can easily enable this by passing in a `api_key` and `client_id` in order to hit the managed url and access the memory api on Metal. Twitter: [@softboyjimbo](https://twitter.com/softboyjimbo) ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @dev2049 @hwchase17

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PR_kwDOIPDwls5RzXJI

Add "name" parameter in Request body under messages for chat completions via OpenAI API

closed

NONE

Add "name" parameter in Request body under messages for chat completions via OpenAI API This adds "name" parameter to the request body inside "messages". For example, when defining HumanMessage or AIMessage, there is now option to add name parameter like this: ```py HumanMessage(content="Hello! what is my name?", name='Ilya') ``` As in OpenAI API: https://platform.openai.com/docs/api-reference/chat/create#chat/create-name  The message dictionaries look like this (of course name parameter is optional): ```py {'role': 'system', 'content': "That's goofy conversation without rules"} {'role': 'user', 'name': 'Spy', 'content': 'Hello! what is my name? And your name?'} {'role': 'assistant', 'name': 'Scout', 'content': "Hi! I won't answer your question, You mad? :P "} {'role': 'assistant', 'name': 'Heavy', 'content': "Bruh man, don't be mean"} {'role': 'user', 'name': 'spy', 'content': 'As you wish, Scout. Off to visit your mom ヾ(•ω•`)o'} {'role': 'system', 'content': "summarize the conversation and list it's participants"} ``` ## Simple test ```py import os from dotenv import load_dotenv load_dotenv() from langchain.llms import OpenAI from langchain.chat_models import ChatOpenAI from langchain import PromptTemplate, LLMChain from langchain.prompts.chat import ( ChatPromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemplate, ) from langchain.schema import ( AIMessage, HumanMessage, SystemMessage ) chat = ChatOpenAI(model_name="gpt-4",temperature=1) # change gpt-4 to gpt-3.5-turbo if you doesn't have gpt-4 access yet messages = [ SystemMessage(content="That's goofy conversation without rules"), HumanMessage(content="Hello! what is my name? And your name?", name='Spy'), AIMessage(content="Hi! I won't answer your question, You mad? :P ", name='Scout'), AIMessage(content="Bruh man, don't be mean", name='Heavy'), HumanMessage(content="As you wish, Scout. Off to visit your mom ヾ(•ω•`)o", name='spy'), SystemMessage(content="summarize the conversation and list it's participants") ] response=chat(messages) print(response.content,end='\n') ``` Response should be smt like this: ``` Participants: User (Spy), Assistant (Scout), Assistant (Heavy) Summary: The User initiates a goofy conversation asking about names. Assistant (Scout) responds playfully by not answering the question, then Assistant (Heavy) advises to not be mean. The User then continues with a witty remark about visiting Assistant (Scout)'s mom. ``` ## Maintainers/contributors who might be interested: Models @hwchase17 @agola11 VectorStores / Retrievers / Memory @dev2049

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PR_kwDOIPDwls5RzB__

bump 187

closed

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PR_kwDOIPDwls5Ry8xh

add more vars to text splitter

closed

COLLABORATOR

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PR_kwDOIPDwls5Ry8Pk

Text splitter regex

closed

COLLABORATOR

# Your PR Title (What it does) <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->

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PR_kwDOIPDwls5Ry29T

Fix wrong class instantiation in docs MMR example

closed

CONTRIBUTOR

# Fix wrong class instantiation in docs MMR example <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> When looking at the Maximal Marginal Relevance ExampleSelector example at https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/mmr.html, I noticed that there seems to be an error. Initially, the `MaxMarginalRelevanceExampleSelector` class is used as an `example_selector` argument to the `FewShotPromptTemplate` class. Then, according to the text, a comparison is made to regular similarity search. However, the `FewShotPromptTemplate` still uses the `MaxMarginalRelevanceExampleSelector` class, so the output is the same. To fix it, I added an instantiation of the `SemanticSimilarityExampleSelector` class, because this seems to be what is intended. ## Who can review? @hwchase17

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PR_kwDOIPDwls5Ryst9

Add texts with embeddings to PGVector wrapper

closed

CONTRIBUTOR

Similar to #1813 for faiss, this PR is to extend functionality to pass text and its vector pair to initialize and add embeddings to the PGVector wrapper. Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @dev2049

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I_kwDOIPDwls5nXKtK

Tracking of time to generate text

closed

CONTRIBUTOR

### Feature request Hey Team, I would like to propose a new feature that will enhance the visibility of the LLM's response time. In addition to providing information about token usage and cost, I suggest incorporating the time taken to generate the text. This additional metric will offer valuable insights into the efficiency and performance of the system. ### Motivation By including the response time, we can provide a comprehensive picture of the different LLM's API's performance, ensuring that we have a more accurate measure of its capabilities. This information will be particularly useful for evaluating and optimizing different LLMs, as it will shed light on the latency of the system. ### Your contribution We can easily implement this by adding additional variables in callbacks of LLM's. I would like to implement this feature. Here the example code: ``` class BaseCallbackHandler: """Base callback handler that can be used to handle callbacks from langchain.""" time_take_by_llm_to_generate_text: float = 0 start_time: float = 0 def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> Any: """Run when LLM starts running.""" self.start_time = datetime.now() def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any: """Run when LLM ends running.""" self.end_time = datetime.now() self.time_take_by_llm_to_generate_text += end_time - start_time ```

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PR_kwDOIPDwls5RyNkI

QuickFix for FinalStreamingStdOutCallbackHandler: Ignore new lines & white spaces

closed

CONTRIBUTOR

# Make FinalStreamingStdOutCallbackHandler more robust by ignoring new lines & white spaces `FinalStreamingStdOutCallbackHandler` doesn't work out of the box with `ChatOpenAI`, as it tokenized slightly differently than `OpenAI`. The response of `OpenAI` contains the tokens `["\nFinal", " Answer", ":"]` while `ChatOpenAI` contains `["Final", " Answer", ":"]`. This PR make `FinalStreamingStdOutCallbackHandler` more robust by ignoring new lines & white spaces when determining if the answer prefix has been reached. Fixes #5433 ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: Tracing / Callbacks - @agola11 Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord: RicChilligerDude#7589

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PR_kwDOIPDwls5RyMSe

simple code update

closed

CONTRIBUTOR

# Update the comments and python logic <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->

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PR_kwDOIPDwls5Rx_wS

make the elasticsearch api support version which below 8.x

closed

CONTRIBUTOR

# Your PR Title (What it does) make the elasticsearch api support version which below 8.x <!-- the api which create index or search in the elasticsearch below 8.x is different with 8.x. When use the es which below 8.x , it will throw error. I fix the problem --> @hwchase17 @dev2049 -->

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PR_kwDOIPDwls5Rxic8

feat: support NebulaGraph DB, add NebulaGraphQAChain

closed

CONTRIBUTOR

# feat: support NebulaGraph DB, add NebulaGraphQAChain Bring the support of [NebulaGraph](http://github.com/vesoft-inc/nebula), the open-source distributed graph database with NebulaGraphQAChain ## Before submitting - [x] a test for the integration - favor unit tests that do not rely on network access. - [x] an example notebook showing its use ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @tomasonjo @vowelparrot

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I_kwDOIPDwls5nWXf8

Issue: LLM callback handler not printing in Docker

closed

NONE

### Issue you'd like to raise. I have a Flask with LangChain setup in docker-compose, and I don't see LLM ChatOpenAI streaming output from CallbackHandlers in console, but everything works when I run it locally without Docker. My CallbackHandler code (StreamingStdOutCallbackHandler also doesn't work): ``` from typing import Any from langchain.callbacks.base import BaseCallbackHandler class StreamingOutput(BaseCallbackHandler): def on_llm_new_token(self, token: str, **kwargs: Any) -> None: print(token, end="", flush=True) ``` - ChatOpenAI has streaming and verbose flags set to true - ConversationChain has verbose flag set to True - Flask is run with `CMD ["flask", "run", "--debug", "--with-threads"]` I tried setting the PYTHONUNBUFFERED env variable but it didn't help - what am I doing wrong? ### Suggestion: _No response_

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I_kwDOIPDwls5nWLxD

Can I connect to my RDBMS?

closed

NONE

Can I connect to my RDBMS? ### Suggestion: _No response_

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I_kwDOIPDwls5nVsvo

Error while importing Langchain

closed

NONE

--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Input In [6], in <cell line: 1>() ----> 1 import langchain File ~\anaconda3\lib\site-packages\langchain\__init__.py:6, in <module> 3 from importlib import metadata 4 from typing import Optional ----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain 7 from langchain.cache import BaseCache 8 from langchain.chains import ( 9 ConversationChain, 10 LLMBashChain, (...) 18 VectorDBQAWithSourcesChain, 19 ) File ~\anaconda3\lib\site-packages\langchain\agents\__init__.py:2, in <module> 1 """Interface for agents.""" ----> 2 from langchain.agents.agent import ( 3 Agent, 4 AgentExecutor, 5 AgentOutputParser, 6 BaseMultiActionAgent, 7 BaseSingleActionAgent, 8 LLMSingleActionAgent, 9 ) 10 from langchain.agents.agent_toolkits import ( 11 create_csv_agent, 12 create_json_agent, (...) 21 create_vectorstore_router_agent, 22 ) 23 from langchain.agents.agent_types import AgentType File ~\anaconda3\lib\site-packages\langchain\agents\agent.py:13, in <module> 10 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union 12 import yaml ---> 13 from pydantic import BaseModel, root_validator 15 from langchain.agents.agent_types import AgentType 16 from langchain.agents.tools import InvalidTool File ~\anaconda3\lib\site-packages\pydantic\__init__.py:2, in init pydantic.__init__() File ~\anaconda3\lib\site-packages\pydantic\dataclasses.py:48, in init pydantic.dataclasses() File ~\anaconda3\lib\site-packages\pydantic\main.py:120, in init pydantic.main() TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'

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I_kwDOIPDwls5nVXO-

the text splitter adds metadata by itself

closed

NONE

### Issue you'd like to raise. I cant find any way to add custom metadata with the character splitter, it adds source as metadata but I cant seem to change it or define what kind of metadata I want ### Suggestion: _No response_

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I_kwDOIPDwls5nVSdc

Connecting to Elastic vector store throws ssl error

closed

NONE

Is there a way to pass parameters to Elasticvectorsearch to disable ssl verification. I tried to add verify_certs=False and ssl_verify=None ; but both didnt work.

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I_kwDOIPDwls5nVONO

HuggingFacePipeline is not loaded correctly

open

CONTRIBUTOR

### System Info langchain==0.0.186 ### Who can help? @hwchase17 ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Running the following code snippet: ```python from langchain import HuggingFacePipeline llm = HuggingFacePipeline.from_model_id(model_id="bigscience/bloom-1b7", task="text-generation", model_kwargs={"temperature":0, "max_length":64}) from langchain import PromptTemplate, LLMChain template = """Question: {question} Answer: Let's think step by step.""" prompt = PromptTemplate(template=template, input_variables=["question"]) llm_chain = LLMChain(prompt=prompt, llm=llm) llm_chain.save("/tmp/hfp/model.yaml") from langchain.chains.loading import load_chain local_loaded_model = load_chain("/tmp/hfp/model.yaml") question = "What is electroencephalography?" local_loaded_model.run(question) ``` Gives the following error: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) File <command-826248432925795>:1 ----> 1 local_loaded_model.run(question) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/chains/base.py:236, in Chain.run(self, callbacks, *args, **kwargs) 234 if len(args) != 1: 235 raise ValueError("`run` supports only one positional argument.") --> 236 return self(args[0], callbacks=callbacks)[self.output_keys[0]] 238 if kwargs and not args: 239 return self(kwargs, callbacks=callbacks)[self.output_keys[0]] File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/chains/base.py:140, in Chain.__call__(self, inputs, return_only_outputs, callbacks) 138 except (KeyboardInterrupt, Exception) as e: 139 run_manager.on_chain_error(e) --> 140 raise e 141 run_manager.on_chain_end(outputs) 142 return self.prep_outputs(inputs, outputs, return_only_outputs) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/chains/base.py:134, in Chain.__call__(self, inputs, return_only_outputs, callbacks) 128 run_manager = callback_manager.on_chain_start( 129 {"name": self.__class__.__name__}, 130 inputs, 131 ) 132 try: 133 outputs = ( --> 134 self._call(inputs, run_manager=run_manager) 135 if new_arg_supported 136 else self._call(inputs) 137 ) 138 except (KeyboardInterrupt, Exception) as e: 139 run_manager.on_chain_error(e) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/chains/llm.py:69, in LLMChain._call(self, inputs, run_manager) 64 def _call( 65 self, 66 inputs: Dict[str, Any], 67 run_manager: Optional[CallbackManagerForChainRun] = None, 68 ) -> Dict[str, str]: ---> 69 response = self.generate([inputs], run_manager=run_manager) 70 return self.create_outputs(response)[0] File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/chains/llm.py:79, in LLMChain.generate(self, input_list, run_manager) 77 """Generate LLM result from inputs.""" 78 prompts, stop = self.prep_prompts(input_list, run_manager=run_manager) ---> 79 return self.llm.generate_prompt( 80 prompts, stop, callbacks=run_manager.get_child() if run_manager else None 81 ) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/llms/base.py:134, in BaseLLM.generate_prompt(self, prompts, stop, callbacks) 127 def generate_prompt( 128 self, 129 prompts: List[PromptValue], 130 stop: Optional[List[str]] = None, 131 callbacks: Callbacks = None, 132 ) -> LLMResult: 133 prompt_strings = [p.to_string() for p in prompts] --> 134 return self.generate(prompt_strings, stop=stop, callbacks=callbacks) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/llms/base.py:191, in BaseLLM.generate(self, prompts, stop, callbacks) 189 except (KeyboardInterrupt, Exception) as e: 190 run_manager.on_llm_error(e) --> 191 raise e 192 run_manager.on_llm_end(output) 193 return output File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/llms/base.py:185, in BaseLLM.generate(self, prompts, stop, callbacks) 180 run_manager = callback_manager.on_llm_start( 181 {"name": self.__class__.__name__}, prompts, invocation_params=params 182 ) 183 try: 184 output = ( --> 185 self._generate(prompts, stop=stop, run_manager=run_manager) 186 if new_arg_supported 187 else self._generate(prompts, stop=stop) 188 ) 189 except (KeyboardInterrupt, Exception) as e: 190 run_manager.on_llm_error(e) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/llms/base.py:436, in LLM._generate(self, prompts, stop, run_manager) 433 new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") 434 for prompt in prompts: 435 text = ( --> 436 self._call(prompt, stop=stop, run_manager=run_manager) 437 if new_arg_supported 438 else self._call(prompt, stop=stop) 439 ) 440 generations.append([Generation(text=text)]) 441 return LLMResult(generations=generations) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-51383c0b-9b1f-48ec-a6a9-ac979dcdc755/lib/python3.10/site-packages/langchain/llms/huggingface_pipeline.py:168, in HuggingFacePipeline._call(self, prompt, stop, run_manager) 162 def _call( 163 self, 164 prompt: str, 165 stop: Optional[List[str]] = None, 166 run_manager: Optional[CallbackManagerForLLMRun] = None, 167 ) -> str: --> 168 response = self.pipeline(prompt) 169 if self.pipeline.task == "text-generation": 170 # Text generation return includes the starter text. 171 text = response[0]["generated_text"][len(prompt) :] TypeError: 'NoneType' object is not callable ``` ### Expected behavior `local_loaded_model.run(question)` should behave the same way as: ```python llm_chain.run(question) ```

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PR_kwDOIPDwls5Rwcgt

Gfrenkel/sql columns option

closed

NONE

# Add column names to SQLDatabase.run() result Description: Current state: SQLDatabase.run() method returns a result from the database after executing a SQL query but there is no option of adding the column names as well to the returned result. PR offer the following solution: Adding a argument "include_column_names_on_query" to the SQLDatabase constructor. By default, this argument will be set to False, but when set to True, SQLDatabase.run() method will add the column names to the result (if the result is not empty). Would love to hear some feedback! - @hwchase17 - @vowelparrot

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PR_kwDOIPDwls5Rwa1b

Add param requests_kwargs for WebBaseLoader

closed

CONTRIBUTOR

# Add param `requests_kwargs` for WebBaseLoader Fixes # (issue) #5483 ## Who can review? @eyurtsev

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PR_kwDOIPDwls5RwaxD

Adds var and class to JS separators

closed

CONTRIBUTOR

# Adds var and class to JS separators Some additional separators for text splitting on JS code.

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I_kwDOIPDwls5nVJCa

[SSL: CERTIFICATE_VERIFY_FAILED] while load from SitemapLoader

closed

CONTRIBUTOR

### System Info langchain: 0.0.181 platform: windows python: 3.11.3 ### Who can help? @eyurtsev ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```py site_loader = SitemapLoader(web_path="https://help.glueup.com/sitemap_index.xml") docs = site_loader.load() print(docs[0]) # ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1002) ``` ### Expected behavior print the frist doc

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PR_kwDOIPDwls5RwaI5

Add option to ignore column names in SQLDatabase.run()

closed

NONE

# Add column names to SQLDatabase.run() result Description: Current state: SQLDatabase.run() method returns a result from the database after executing a SQL query but there is no option of adding the column names as well to the returned result. PR offer the following solution: Adding a argument "ignore_column_names_on_query" to the SQLDatabase constructor. By default, the argument is set to True, but when set to False, SQLDatabase.run() method will add the column names to the result (if the result is not empty). Would love to hear some feedback! - @hwchase17 - @vowelparrot

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I_kwDOIPDwls5nURQX

[Feature Request] Supoprts document loader caching

closed

NONE

### Feature request I want langchain to implement caching for document loaders in a way similar to how it caches LLM calls, like this: ```python from langchain.cache import InMemoryCache langchain.document_loader_cache = InMemoryCache() ``` ### Motivation Loading from certain documents with langchain document loader can be an expensive operation (for example, I implemneted a custom PDF loader using OCR that's slow, or loaders that involves network calls). ### Your contribution If langchain would accept such a PR, I'd try to implement the logic and file a PR.

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PR_kwDOIPDwls5RvodF

code splitter docs

closed

COLLABORATOR

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PR_kwDOIPDwls5RvnHw

Update youtube.py - Fix metadata validation error in YoutubeLoader

closed

CONTRIBUTOR

This commit addresses a ValueError occurring when the YoutubeLoader class tries to add datetime metadata from a YouTube video's publish date. The error was happening because the ChromaDB metadata validation only accepts str, int, or float data types. In the `_get_video_info` method of the `YoutubeLoader` class, the publish date retrieved from the YouTube video was of datetime type. This commit fixes the issue by converting the datetime object to a string before adding it to the metadata dictionary. Additionally, this commit introduces error handling in the `_get_video_info` method to ensure that all metadata fields have valid values. If a metadata field is found to be None, a default value is assigned. This prevents potential errors during metadata validation when metadata fields are None. The file modified in this commit is youtube.py. # Your PR Title (What it does) <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->

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PR_kwDOIPDwls5Rvk5w

make BaseEntityStore inherit from BaseModel

closed

CONTRIBUTOR

# Make BaseEntityStore inherit from BaseModel This enables initializing InMemoryEntityStore by optionally passing in a value for the store field. ## Who can review? It's a small change so I think any of the reviewers can review, but tagging @dev2049 who seems most relevant since the change relates to Memory.

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PR_kwDOIPDwls5RveBJ

move schema to a directory

closed

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I_kwDOIPDwls5nUA_I

IndexError: list index out of range when use Chroma.from_documents

closed

NONE

### System Info Lang Chain 0.0.186 Mac OS Ventura Python 3.10 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction why i got IndexError: list index out of range when use Chroma.from_documents import os from langchain.document_loaders import BiliBiliLoader from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.text_splitter import RecursiveCharacterTextSplitter os.environ["OPENAI_API_KEY"] = "***" loader = BiliBiliLoader(["https://www.bilibili.com/video/BV18o4y137n1/"]) documents = loader.load() text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=20 ) documents = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() db = Chroma.from_documents(documents, embeddings, persist_directory="./db") db.persist() Traceback (most recent call last): File "/bilibili/bilibili_embeddings.py", line 28, in <module> db = Chroma.from_documents(documents, embeddings, persist_directory="./db") File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 422, in from_documents return cls.from_texts( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 390, in from_texts chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 160, in add_texts self._collection.add( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chromadb/api/models/Collection.py", line 103, in add ids, embeddings, metadatas, documents = self._validate_embedding_set( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chromadb/api/models/Collection.py", line 354, in _validate_embedding_set ids = validate_ids(maybe_cast_one_to_many(ids)) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/chromadb/api/types.py", line 82, in maybe_cast_one_to_many if isinstance(target[0], (int, float)): IndexError: list index out of range ### Expected behavior index gen succefully in the persist_directory

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I_kwDOIPDwls5nT2bZ

Getting only the instance of the vector store without adding text

closed

CONTRIBUTOR

### Feature request Hi, Currently the from_documents method will add the embeddings and then return the instance of the store. Why don't we have a method to just return the store. This is useful when I already have a loaded vector store and I just need the instance of the store. It will be like the below code without the _store.add_texts_ ``` store = cls( connection_string=connection_string, collection_name=collection_name, embedding_function=embedding, distance_strategy=distance_strategy, pre_delete_collection=pre_delete_collection, ) store.add_texts(texts=texts, metadatas=metadatas, ids=ids, **kwargs) return store ``` ### Motivation This is required when I already have a loaded vector store ### Your contribution If this change is acceptable, I can add this functionality and create a PR

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I_kwDOIPDwls5nTqIs

llm_chain.llm.save("llm.json") # method not found

closed