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PR_kwDOIPDwls5kHJ-R

docs: fix self query diagram

closed

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I_kwDOIPDwls58Hwz7

In RunnableSequence Action input / tool_input incorrectly set, ultimately causing incorrect answers, results

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` # No code provided # LangSmith trace link in Description ``` ### Description I test a sample code of CrewAI, which is based on Langchain and I found, again :( that tool_input is incorrectly set, which causes incorrect answers. I track it in LangSmith and here is the RunnableSequence's input: ``` input: Investigate which AI / LLM assisted IDE assistant can assist coding related questions not only related to the selected code or the opened file, but all the project code files, the console and terminal messages. Check also the most recent IDEs, not only the most popular ones. Your final answer MUST be a full analysis report tool_names: Google Search tools: "Google Search: Search Google for recent results." chat_history: "" intermediate_steps: [] ``` and output: ``` output: tool: Google Search tool_input: AI / LLM assisted IDE assistant for coding related questions log: |- Thought: Do I need to use a tool? Yes Action: Google Search Action Input: "AI / LLM assisted IDE assistant for coding related questions" type: AgentAction ``` so we can see the original task of: _**"Investigate which AI / LLM assisted IDE assistant can assist coding related questions not only related to the selected code or the opened file, but all the project code files, the console and terminal messages. Check also the most recent IDEs, not only the most popular ones. Your final answer MUST be a full analysis report"**_ was transformed to a Google search for: _**"AI / LLM assisted IDE assistant for coding related questions"**_ which obviously doesn't provide the search results relevant to original task! The trace with Zephyr-7B via Hugging Face: https://smith.langchain.com/public/d4076fff-78d7-48ab-bdfe-d463bd1ffcb3/r The same with gpt-4: https://smith.langchain.com/public/1fa8da61-6cc9-448c-8aa3-fd75d930e623/r **So what and where should we finetune, modify to keep search term or any other tool_input correct, without huge distortion?** ### System Info Windows 10 Python 3.11.6 langchain-core: 0.1.10 langchain-community: 0.0.12 crewai: 0.1.32 ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kG7vR

docs: fix documentation broken link in integrations chroma

closed

CONTRIBUTOR

- **Description:** Fixed broken link in the documentation for Chroma., - **Issue:** - **Dependencies:**

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PR_kwDOIPDwls5kG3dJ

add demo website mention

closed

NONE

<!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5kGs_g

community: add new gpt-3.5-turbo-1106 finetuned for cost calculation

closed

CONTRIBUTOR

**Description:** Added the new gpt-3.5-turbo-1106 for **finetuned** cost calculation, **Issue:** no issue found open By the information in OpenAI the pricing is the same as the older model (0613)

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I_kwDOIPDwls58HKyk

RetrievalQAWithSourcesChain result sources is not deterministic

open

CONTRIBUTOR

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code I implemented a custom retriever like this: ``` class CustomUserRetriever(BaseRetriever): vectorstore: VectorStore username: str # New attribute to store the filename def clean_metadata(self, doc): metadata = doc.metadata return { # Add a button to clear chat history "Title": metadata["title"], "source": metadata["title"], "chunk_id": metadata["chunk_id"] } def get_relevant_documents(self, query): docs = [] for doc in self.vectorstore.similarity_search(query, search_type="hybrid", k=5, filters=f"search.ismatch('{self.username.split('@')[0]}', 'usernames_result') "): cleaned_metadata = self.clean_metadata(doc) docs.append(Document( page_content=doc.page_content, metadata=cleaned_metadata)) return docs ``` And then I created a custom functionality which should return the answer, and the source of the answer. (document name with extension) ``` retriever = CustomUserRetriever(vectorstore=vector_store, username=user_name) chain = RetrievalQAWithSourcesChain.from_chain_type(llm, chain_type="stuff", memory=memory, retriever=retriever, return_source_documents=True ) #result = chain({"question": user_question}) result = chain({"question":user_question}, return_only_outputs=True) ``` THen I ask a question which should get a response from 2 document chunks, and the answer is generated correctly. However asking the same question multiple times, sometimes I get an empty source and sometimes I get a list of the source documents,   ### Description I want to get answers from internal documents, but the document retriever returns by similarity chunks that are not relevant to the generated answer, so those ones should be removed, sometimes it works! I asked a question and the chunks are returned, sometimes I get an empty source list. ### System Info python 3.11 langchain 0.0.353 openai 1.6.1 ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async

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I_kwDOIPDwls58G8tp

langchain 0.0.27 should NOT be installed on python 3.6.5

closed

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code This is a minor issue with metadata for pip. I opened a ticket against pip. https://github.com/pypa/pip/issues/12476 But I was told that I should report the issue to this project. [langchain_0.0.27.log](https://github.com/langchain-ai/langchain/files/13940154/langchain_0.0.27.log) If there existed a langchain version which doesn't use future feature annotations, install it. Otherwise, prompt the user that no suitable version. ### Description I installed langchain 0.0.27 on python 3.6.5 on my Windows 10 successfully. But I run into an issue when I tried import the library. from langchain.llms import Ollama SyntaxError: future feature annotations is not defined [PEP-563](https://www.python.org/dev/peps/pep-0563/#enabling-the-future-behavior-in-python-3-7)future annotations are introduced in Python 3.7. That means langchain 0.0.27 which depends on future feature annotations should NOT be installed on Python 3.6.5. ### System Info pip 21.3.1 python 3.6.5/Windows 10 ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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I_kwDOIPDwls58G4M2

Using faiss.from_documents require tokenizer pad_token

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` embeddings = HuggingFaceEmbeddings( model_name="mistralai/Mistral-7B-v0.1", # Provide the pre-trained model's path ) cache_embeddings = CacheBackedEmbeddings.from_bytes_store(embeddings, cached_dir) vectorstore = FAISS.from_documents(docs, cache_embeddings) ``` I use this code. I met this error. Why occur this occur? I look around all. I can't fix ti. so Sad ㅠㅠㅠ Could you explain me how to get fix it? ``` ValueError Traceback (most recent call last) Cell In[8], [line 1](vscode-notebook-cell:?execution_count=8&line=1) ----> [1](vscode-notebook-cell:?execution_count=8&line=1) vectorstore = FAISS.from_documents(docs, cache_embeddings) [3](vscode-notebook-cell:?execution_count=8&line=3) retriver = vectorstore.as_retriever(reduce_k_below_max_tokens=True) [4](vscode-notebook-cell:?execution_count=8&line=4) # retriver = vectorstore.as_retriever() File [~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:508](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:508), in VectorStore.from_documents(cls, documents, embedding, **kwargs) [506](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:506) texts = [d.page_content for d in documents] [507](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:507) metadatas = [d.metadata for d in documents] --> [508](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_core/vectorstores.py:508) return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs) File [~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:913](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:913), in FAISS.from_texts(cls, texts, embedding, metadatas, ids, **kwargs) [886](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:886) @classmethod [887](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:887) def from_texts( [888](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:888) cls, (...) [893](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:893) **kwargs: Any, [894](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:894) ) -> FAISS: [895](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:895) """Construct FAISS wrapper from raw documents. [896](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:896) [897](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:897) This is a user friendly interface that: (...) [911](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:911) faiss = FAISS.from_texts(texts, embeddings) [912](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/langchain_community/vectorstores/faiss.py:912) """ ... (...) [2719](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:2719) and (max_length % pad_to_multiple_of != 0) [2720](https://vscode-remote+ssh-002dremote-002bssh-002eddapp-002eio.vscode-resource.vscode-cdn.net/home/datadriven/ds.kang/rag/~/anaconda3/envs/rag/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:2720) ): ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})` ``` ### Description I use this code. I met this error. Why occur this occur? I look around all. I can't fix ti. so Sad ㅠㅠㅠ Could you explain me how to get fix it? ### System Info langchain 0.1.0 langchain-community 0.0.12 langchain-core 0.1.10 langsmith 0.0.80 ### Related Components - [ ] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kGRSV

community: Ollama - Parameter structure to follow official documentation

closed

CONTRIBUTOR

## Feature - Follow parameter structure as per official documentation - top level parameters (e.g. model, system, template) will be passed as top level parameters - other parameters will be sent in options unless options is provided  ## Tests - Test if top level parameters handled properly - Test if parameters that are not top level parameters are handled as options - Test if options is provided, it will be passed as is

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I_kwDOIPDwls58Go6k

AttributeError: 'list' object has no attribute 'embed_query'

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` Python # Chunking the sentence with fixed size from langchain.text_splitter import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20) all_splits = text_splitter.split_documents(documents) ``` ``` Python from langchain_community.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-base-en" model_kwargs = {"device": "cpu"} encode_kwargs = {"normalize_embeddings": True} embeddings = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) ``` ``` Python # Creating New Index: !CREATE VECTOR INDEX KG_QuestionAnswering IF NOT EXISTS FOR (n:LabelName) ON (n.propertyName) OPTIONS {dimension: 1024}; ``` ``` Python index_name="KG_QuestionAnswering" # Instantiate Neo4j vector from documents store = Neo4jVector.from_existing_index( documents, HuggingFaceBgeEmbeddings(), url=os.environ["NEO4J_URI"], username=os.environ["NEO4J_USERNAME"], password=os.environ["NEO4J_PASSWORD"] ) ``` ### Description ``` AttributeError Traceback (most recent call last) [<ipython-input-24-4e3c558558b3>](https://localhost:8080/#) in <cell line: 3>() 1 index_name="KG_QuestionAnswering" 2 # Instantiate Neo4j vector from documents ----> 3 store = Neo4jVector.from_existing_index( 4 documents, 5 HuggingFaceBgeEmbeddings(), 1 frames [/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in __init__(self, embedding, search_type, username, password, url, keyword_index_name, database, index_name, node_label, embedding_node_property, text_node_property, distance_strategy, logger, pre_delete_collection, retrieval_query, relevance_score_fn) 236 self.search_type = search_type 237 # Calculate embedding dimension --> 238 self.embedding_dimension = len(embedding.embed_query("foo")) 239 240 # Delete existing data if flagged AttributeError: 'list' object has no attribute 'embed_query' ``` ### System Info ``` YAML Windows: 11 pip == 23.3.1 python == 3.10.10 langchain == 0.1.0 transformers == 4.36.2 sentence_transformers == 2.2.2 Neo4j == 5 ``` ### Related Components - [ ] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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I_kwDOIPDwls58GLqy

ElasticVectorSearch Error similarity_search openai.NotFoundError: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code My code was fully working till i updated from 0.0.259 to 0.1.0 langchain ``` embeddings = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"], model=os.environ["AZUREOPENAI_EMBEDDINGS_MODEL"], deployment=os.environ["AZUREOPENAI_EMBEDDINGS_DEPLOYMENT"]) db = ElasticVectorSearch( embedding=embeddings, elasticsearch_url=url, index_name=os.environ["AZUREOPENAI_INDEX_NAME"], ssl_verify={"ca_certs": os.environ["APP_CERT_PATH"]} ) docs = db.similarity_search(query=query, # new k=10) # Here is when it crashes ``` ### Description The code is crashing giving a weird error and It looks it is because of similarity_search doesnt work in the new version ### System Info aiohttp==3.9.1 aiosignal==1.3.1 annotated-types==0.6.0 anyio==4.2.0 async-timeout==4.0.3 attrs==23.2.0 certifi==2023.11.17 charset-normalizer==3.3.2 click==8.1.7 dataclasses-json==0.6.3 distro==1.9.0 elastic-transport==8.11.0 elasticsearch==8.11.1 exceptiongroup==1.2.0 fastapi==0.109.0 frozenlist==1.4.1 greenlet==3.0.3 h11==0.14.0 httpcore==1.0.2 httpx==0.26.0 idna==3.6 jsonpatch==1.33 jsonpointer==2.4 langchain==0.1.0 langchain-community==0.0.12 langchain-core==0.1.10 langsmith==0.0.80 marshmallow==3.20.2 multidict==6.0.4 mypy-extensions==1.0.0 numpy==1.26.3 openai==1.7.2 packaging==23.2 pydantic==2.5.3 pydantic_core==2.14.6 PyYAML==6.0.1 regex==2023.12.25 requests==2.31.0 sniffio==1.3.0 SQLAlchemy==2.0.25 starlette==0.35.1 tenacity==8.2.3 tiktoken==0.5.2 tqdm==4.66.1 typing-inspect==0.9.0 typing_extensions==4.9.0 urllib3==2.1.0 uvicorn==0.25.0 yarl==1.9.4 ### 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 - [X] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kFtL6

community: add StreamingAPIGatewayWebSocketCallbackHandler

open

NONE

- **Description:** add a community callback handler `StreamingAPIGatewayWebSocketCallbackHandler`, which is useful for serverless applications on AWS to streaming LLM's response to clients via AWS API Gateway WebSocket API and AWS Lambda. - **Issue:** N/A - **Dependencies:** N/A - **Twitter handle:** N/A

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I_kwDOIPDwls58FsT9

Unable to install langchain-google-genai

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code pip install langchain-google-genai ### Description I am trying to install langchain-google-genai package, however getting below error: ERROR: Could not find a version that satisfies the requirement langchain-google-genai==0.0.5 (from versions: none) ERROR: No matching distribution found for langchain-google-genai==0.0.5 ### System Info python 3.8.3 ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kFO9j

docs: add milvus multitenancy doc

closed

CONTRIBUTOR

- **Description:** add milvus multitenancy doc, it is an example for this [pr](https://github.com/langchain-ai/langchain/pull/15740) . - **Issue:** no, - **Dependencies:** no, - **Twitter handle:** no

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I_kwDOIPDwls58FiWY

Multiple GPU - Degradation over time

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ```llm = LlamaCpp(model_path=model_loc, n_ctx=0, verbose=True, n_gpu_layers=-1) chain = LLMChain(llm=llm, prompt=prompt) result = chain(inputs={"context": context, "question": question}) answer = result["text"] ``` I do use the PromptTemplate, but this is not relevant to my issue. ### Description I am using 2x A100 GPU. What I have noticed is that there is GPU degradation over time. This is my spool up data: ``` ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 2 CUDA devices: Device 0: NVIDIA A100-PCIE-40GB, compute capability 8.0, VMM: no Device 1: NVIDIA A100-PCIE-40GB, compute capability 8.0, VMM: no ``` In my code, the above is part of an API that takes in a request and then responds to it. I am making 5 calls to the API one after another, and collating the data for a final summarisation call. I am not overly impressed with the speeds here in any event - so if anyone has any tips for improving, I don't think 17.25 tokens is very good for dual A100s. ``` llama_print_timings: eval time = 57.98 ms / 1 runs ( 57.98 ms per token, 17.25 tokens per second) llama_print_timings: total time = 6722.12 ms llama_print_timings: eval time = 4804.66 ms / 70 runs ( 68.64 ms per token, 14.57 tokens per second) llama_print_timings: total time = 17722.48 ms llama_print_timings: eval time = 5950.61 ms / 77 runs ( 77.28 ms per token, 12.94 tokens per second) llama_print_timings: total time = 23459.58 ms ``` I would have expected the "overall" time to change but the actual tokens per second to remain somewhat consistent - but instead it just gets worse. I also created a GPU monitor tool, and in that tool, the first call is utilising 25% and 20% for GPU 1 and 2 - but then it goes down to 20% and 17% and then 16% and 15%, and when making a LOT of calls, I have seen it as low as 9% on each GPU - at this point the response time is a minute plus. Why am I writing a bug report here? Sounds like a you problem....because I ran it with Llama-cpp-python - without Langchain and this was not a problem, there was no degradation over time. ### System Info Wrinkles.... I am using Window 10, inside a VMWare Virtual Machine. Python, Langchain, Llama-cpp-python is all up to date, as are the Nvidia drivers and CUDA toolkit (12.2). As noted above - this only occurs when using langchain - not when doing the same workflow, without langchain. ### 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

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I_kwDOIPDwls58FTdE

DOC: <Please write a comprehensive title after the 'DOC: ' prefix>

open

NONE

### Issue with current documentation: https://python.langchain.com/docs/integrations/text_embedding/together import is incorrect in documentation ```python from langchain_together.embeddings import TogetherEmbeddings embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval") ``` ### Idea or request for content: update to: ```python from langchain_together import TogetherEmbeddings <------------ this line changed embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval") ```

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PR_kwDOIPDwls5kE0vM

community: Fixing WARNING by providing embedding_function in collection creation

open

NONE

**Description:** Resolved the WARNING message that occurred due to the absence of the embedding_function parameter during the creation of a collection. Updated the code to include the embedding_function parameter in the collection creation process. **Issue:** #15400 **Dependencies:** None

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I_kwDOIPDwls58FHK-

langchain_community: SQL Database - Don't assume "PostgreSQL" in the else

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code Following code in https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/utilities/sql_database.py from line 386: ``` with self._engine.begin() as connection: # type: Connection if self._schema is not None: if self.dialect == "snowflake": connection.exec_driver_sql( "ALTER SESSION SET search_path = %s", (self._schema,) ) elif self.dialect == "bigquery": connection.exec_driver_sql("SET @@dataset_id=?", (self._schema,)) elif self.dialect == "mssql": pass elif self.dialect == "trino": connection.exec_driver_sql("USE ?", (self._schema,)) elif self.dialect == "duckdb": # Unclear which parameterized argument syntax duckdb supports. # The docs for the duckdb client say they support multiple, # but `duckdb_engine` seemed to struggle with all of them: # https://github.com/Mause/duckdb_engine/issues/796 connection.exec_driver_sql(f"SET search_path TO {self._schema}") elif self.dialect == "oracle": connection.exec_driver_sql( f"ALTER SESSION SET CURRENT_SCHEMA = {self._schema}" ) elif self.dialect == "sqlany": # If anybody using Sybase SQL anywhere database then it should not # go to else condition. It should be same as mssql. pass else: # postgresql and other compatible dialects connection.exec_driver_sql("SET search_path TO %s", (self._schema,)) ``` ### Description Can we change the code of line 412: ``` else: # postgresql and other compatible dialects connection.exec_driver_sql("SET search_path TO %s", (self._schema,)) ``` to ``` elif self.dialect == "postgresql": connection.exec_driver_sql("SET search_path TO %s", (self._schema,)) else: pass # Do nothing ``` It is in my opinion simply wrong to assume that any other DB will work like PostgreSQL. And it's not possible to cut that statement in the dialect easily. If we need more `elif`, so be it. ### System Info Python 3.10 - it's independent of the Python version or environment. ### 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

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PR_kwDOIPDwls5kEkVj

langchain: Allow OpenSearch Query Translator to correctly work with Date types

open

NONE

**Description:** Fixes an issue where the Date type in an OpenSearch Self Querying Retriever would fail to generate a valid query **Issue:** https://github.com/langchain-ai/langchain/issues/14225

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I_kwDOIPDwls58E3i-

SessionExpired: Failed to read from defunct connection IPv4Address(('ae3cd622.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.64.110', 7687)))

closed

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` from langchain.embeddings import HuggingFaceEmbeddings neo4j_db = Neo4jVector.from_documents( documents, HuggingFaceEmbeddings(), url=os.environ["NEO4J_URI"], username=os.environ["NEO4J_USERNAME"], password=os.environ["NEO4J_PASSWORD"], database="neo4j", index_name="graphdb", node_label="huggingface_dataset", text_node_property="info", embedding_node_property="vector", create_id_index=True, ) ``` ### Description ``` ERROR:neo4j.io:Failed to read from defunct connection IPv4Address(('ae3cd622.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.64.110', 7687))) --------------------------------------------------------------------------- OSError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in _buffer_one_chunk(self) 50 # Determine the chunk size and skip noop ---> 51 receive_into_buffer(self._socket, self._buffer, 2) 52 chunk_size = self._buffer.pop_u16() 16 frames [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in receive_into_buffer(sock, buffer, n_bytes) 327 if n == 0: --> 328 raise OSError("No data") 329 buffer.used += n OSError: No data The above exception was the direct cause of the following exception: SessionExpired Traceback (most recent call last) [<ipython-input-43-6c577b606d00>](https://localhost:8080/#) in <cell line: 1>() ----> 1 neo4j_db = Neo4jVector.from_documents( 2 documents, 3 HuggingFaceEmbeddings(), 4 url=os.environ["NEO4J_URI"], 5 username=os.environ["NEO4J_USERNAME"], [/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in from_documents(cls, documents, embedding, distance_strategy, ids, **kwargs) 815 metadatas = [d.metadata for d in documents] 816 --> 817 return cls.from_texts( 818 texts=texts, 819 embedding=embedding, [/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in from_texts(cls, texts, embedding, metadatas, distance_strategy, ids, **kwargs) 679 embeddings = embedding.embed_documents(list(texts)) 680 --> 681 return cls.__from( 682 texts, 683 embeddings, [/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in __from(cls, texts, embeddings, embedding, metadatas, ids, create_id_index, search_type, **kwargs) 471 ) 472 --> 473 store.add_embeddings( 474 texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs 475 ) [/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in add_embeddings(self, texts, embeddings, metadatas, ids, **kwargs) 520 } 521 --> 522 self.query(import_query, params=parameters) 523 524 return ids [/usr/local/lib/python3.10/dist-packages/langchain_community/vectorstores/neo4j_vector.py](https://localhost:8080/#) in query(self, query, params) 272 with self._driver.session(database=self._database) as session: 273 try: --> 274 data = session.run(query, params) 275 return [r.data() for r in data] 276 except CypherSyntaxError as e: [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/work/session.py](https://localhost:8080/#) in run(self, query, parameters, **kwargs) 311 bookmarks = self._get_bookmarks() 312 parameters = dict(parameters or {}, **kwargs) --> 313 self._auto_result._run( 314 query, parameters, self._config.database, 315 self._config.impersonated_user, self._config.default_access_mode, [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/work/result.py](https://localhost:8080/#) in _run(self, query, parameters, db, imp_user, access_mode, bookmarks, notifications_min_severity, notifications_disabled_categories) 179 self._pull() 180 self._connection.send_all() --> 181 self._attach() 182 183 def _pull(self): [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/work/result.py](https://localhost:8080/#) in _attach(self) 296 if self._exhausted is False: 297 while self._attached is False: --> 298 self._connection.fetch_message() 299 300 def _buffer(self, n=None): [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in inner(*args, **kwargs) 176 def inner(*args, **kwargs): 177 try: --> 178 func(*args, **kwargs) 179 except (Neo4jError, ServiceUnavailable, SessionExpired) as exc: 180 assert not asyncio.iscoroutinefunction(self.__on_error) [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_bolt.py](https://localhost:8080/#) in fetch_message(self) 844 845 # Receive exactly one message --> 846 tag, fields = self.inbox.pop( 847 hydration_hooks=self.responses[0].hydration_hooks 848 ) [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in pop(self, hydration_hooks) 70 71 def pop(self, hydration_hooks): ---> 72 self._buffer_one_chunk() 73 try: 74 size, tag = self._unpacker.unpack_structure_header() [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in _buffer_one_chunk(self) 66 ) as error: 67 self._broken = True ---> 68 Util.callback(self.on_error, error) 69 raise 70 [/usr/local/lib/python3.10/dist-packages/neo4j/_async_compat/util.py](https://localhost:8080/#) in callback(cb, *args, **kwargs) 116 def callback(cb, *args, **kwargs): 117 if callable(cb): --> 118 return cb(*args, **kwargs) 119 120 @staticmethod [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_bolt.py](https://localhost:8080/#) in _set_defunct_read(self, error, silent) 870 self.unresolved_address, self.server_info.address 871 ) --> 872 self._set_defunct(message, error=error, silent=silent) 873 874 def _set_defunct_write(self, error=None, silent=False): [/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_bolt.py](https://localhost:8080/#) in _set_defunct(self, message, error, silent) 922 else: 923 if error: --> 924 raise SessionExpired(message) from error 925 else: 926 raise SessionExpired(message) SessionExpired: Failed to read from defunct connection IPv4Address(('ae3cd622.databases.neo4j.io', 7687)) (ResolvedIPv4Address(('34.126.64.110', 7687))) ``` ### System Info ``` Windows: 11 pip == 23.3.1 python == 3.10.10 langchain == 0.1.0 transformers == 4.36.2 sentence_transformers == 2.2.2 Neo4j == 5 ``` ### Related Components - [ ] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kEQbE

community: fix a bug that mistakenly handle zip iterator in FAISS.from_embeddings

closed

CONTRIBUTOR

**Description**: `zip` is iterator that will only produce result once, so the previous code will cause the `embeddings` to be an empty list. **Issue**: I could not find a related issue. **Dependencies**: this PR does not introduce or affect dependencies.

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PR_kwDOIPDwls5kD8dR

langchain, community: Implement GraphDB QA Chain

open

NONE

- **Description:** Implement GraphDB QA Chain

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PR_kwDOIPDwls5kDKza

langchain: Indexing with `ParentDocumentRetriever`

open

CONTRIBUTOR

- **Description:** Support using indexing along with `ParentDocumentRetriever`. In short, when you create a `ParentDocumentRetriever`, you can provide the arguments you would have provided to `index`. Then when you call `.add_documents`, `index` will be called with the provided arguments instead of directly persisting to the vector store. This change also allows you to optionally provide a list of text splitters to `.add_documents` (being the same length as the list of documents), in case a single text splitter type for all docs that go through the receiver isn't appropriate.

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I_kwDOIPDwls58CyK6

retriever=retriever has a yellow underline, unable to perform the retrieval of vector database function

open

NONE

### Issue with current documentation: import os from langchain.chat_models import ChatOpenAI from langchain.chains import ConversationalRetrievalChain from langchain.memory import ConversationTokenBufferMemory from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores.qdrant import Qdrant from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder, \ HumanMessagePromptTemplate from qdrant_client import QdrantClient os.environ['OPENAI_API_KEY'] = "key" client = QdrantClient(host="192.168.0.312", port=6333) COLLECTION_NAME = "embed" embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") vectorstore = Qdrant( client=client, collection_name=COLLECTION_NAME, embeddings=embeddings ) prompt = ChatPromptTemplate( messages=[ SystemMessagePromptTemplate.from_template( "You are robot." ), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{question}"), ] ) llm_name = "gpt-3.5-turbo" llm = ChatOpenAI(model_name=llm_name, temperature=0) memory_token_limit = 100 retriever = vectorstore.as_retriever() memory = ConversationTokenBufferMemory( llm=llm, prompt=prompt, max_token_limit=int(memory_token_limit), memory_key="chat_history", return_messages=True, ) qa = ConversationalRetrievalChain.from_llm( llm=llm, retriever=retriever, memory=memory, verbose=True, ) chat_history = [] while True: memory.load_memory_variables({"chat_history": chat_history}) question = input('提問：') result = qa.run({'question': question, 'chat_history': chat_history}) print(result) chat_history.append([f'User: {question}', f'Ai: {result}']) print(chat_history) if question.lower() == 'bye': break  ### Idea or request for content: During each round of questioning, OpenAI will retrieve and reference similar conversation histories from a vector database before providing a response

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PR_kwDOIPDwls5kCqRW

community: New documents loader for visio files (with extension .vsdx)

closed

NONE

**Description** : New documents loader for visio files (with extension .vsdx) A [visio file](https://fr.wikipedia.org/wiki/Microsoft_Visio) (with extension .vsdx) is associated with Microsoft Visio, a diagram creation software. It stores information about the structure, layout, and graphical elements of a diagram. This format facilitates the creation and sharing of visualizations in areas such as business, engineering, and computer science. A Visio file can contain multiple pages. Some of them may serve as the background for others, and this can occur across multiple layers. This **loader** extracts the textual content from each page and its associated pages, enabling the extraction of all visible text from each page, similar to what an OCR algorithm would do. **Dependencies** : xmltodict package

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PR_kwDOIPDwls5kCGxL

fix output parsers navigation url

closed

CONTRIBUTOR

Output parsers documentation have broken links. [output parsers](https://python.langchain.com/docs/modules/model_io/output_parsers/) Corrected the prefix for all links.

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PR_kwDOIPDwls5kCDD_

fix(langchain): force ReAct agent to stop after final answer

open

CONTRIBUTOR

- **Description:** ReAct agents often fail when used with completion models because they don't stop after providing the final answer. Specifically, the chain continues because the LLM hallucinates newer questions. This PR makes "Question" a default stop sequence for a react agent. <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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I_kwDOIPDwls58B7ta

WeaviateHybridSearchRetriever docs result in error

open

NONE

### Issue with current documentation: I'm basically running the same code as [Weaviate Hybrid Search](https://python.langchain.com/docs/integrations/retrievers/weaviate-hybrid): ```python import os import dotenv import weaviate from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever from langchain.schema import Document dotenv.load_dotenv() # using an existing weaviate database (definitely works with llama-index) client = weaviate.Client( url="http://34.170.94.90", auth_client_secret=weaviate.AuthApiKey(os.environ["WEAVIATE_API_KEY"]), additional_headers={ "X-Openai-Api-Key": os.getenv("OPENAI_API_KEY"), }, ) retriever = WeaviateHybridSearchRetriever( client=client, index_name="LangChain", text_key="text", attributes=[], create_schema_if_missing=True, ) ``` The error: ``` UnexpectedStatusCodeException Traceback (most recent call last) Cell 4 line 1 ----> [1] retriever = WeaviateHybridSearchRetriever( [2] client=client, [3] index_name="LangChain", [4] text_key="text", [5] attributes=[], [6] create_schema_if_missing=True, [7] File usr/local/lib/python3.9/site-packages/langchain_core/load/serializable.py:107 /usr/local/lib/python3.9/site-packages/langchain_core/load/serializable.py:107), in Serializable.__init__(self, **kwargs) 106 def __init__(self, **kwargs: Any) -> None: --> 107 super().__init__(**kwargs) 108 self._lc_kwargs = kwargs File [usr/local/lib/python3.9/site-packages/pydantic/v1/main.py:339, in BaseModel.__init__(__pydantic_self__, **data) 333 """ 334 Create a new model by parsing and validating input data from keyword arguments. 335 336 Raises ValidationError if the input data cannot be parsed to form a valid model. 337 """ 338 # Uses something other than `self` the first arg to allow "self" as a settable attribute --> 339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data) 340 if validation_error: ... 814 raise RequestsConnectionError("Class may not have been created properly.") from conn_err 815 if response.status_code != 200: --> 816 raise UnexpectedStatusCodeException("Create class", response) UnexpectedStatusCodeException: Create class! Unexpected status code: 422, with response body: {'error': [{'message': 'vectorizer: no module with name "text2vec-openai" present'}]}. ``` I'm using: ``` python-dotenv==1.0.0 langchain==0.1.0 openai==1.7.2 langchain-openai==0.0.2.post1 weaviate-client==3.26.0 ``` ### Idea or request for content: It appears that the docs are out-of-date, at least for: ``` langchain==0.1.0 openai==1.7.2 weaviate-client==3.26.0 ```

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I_kwDOIPDwls58B4bg

unclear docs for using existing Weaviate database

open

NONE

### Issue with current documentation: The [Weaviate docs](https://python.langchain.com/docs/integrations/vectorstores/weaviate) just show examples of using `Weaviate.from_documents()` or `Weaviate.from_texts()`, but do not describe how to use an existing database with embedded documents already stored in the database. Moreover, the descriptions of the `index_name` or `text_key` that are required when calling `Weaviate()` are not described anywhere in the [Weaviate docs](https://python.langchain.com/docs/integrations/vectorstores/weaviate), and `index_name` seems to be very misleading, given that it should be the name of the Weaviate *class*. A naive attempt of: ```python import os import dotenv import weaviate from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_community.vectorstores import Weaviate from langchain_core.runnables import RunnablePassthrough dotenv.load_dotenv() llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo") client = weaviate.Client( url="http://34.170.94.90", auth_client_secret=weaviate.AuthApiKey(os.environ["WEAVIATE_API_KEY"]) ) db = Weaviate(client, 'my_class_name', 'text') retriever = db.as_retriever() chain = ( {"context": retriever, "question": RunnablePassthrough()} | prompt | llm | output_parser ) chain.invoke({"input" : "My particular query"}) ``` Results in the error: ``` ValueError: Error during query: [{'locations': [{'column': 46, 'line': 1}], 'message': 'Syntax Error GraphQL request (1:46) Expected Name, found String "input"\n\n1: {Get{my_class_name(limit: 4 nearText: {concepts: [{"input": "My particular query"}]} ){text}}}\n ^\n', 'path': None}] ``` ...which isn't too helpful on understanding the issue. Note: I did actually use the correct class name instead of `my_class_name`. ### Idea or request for content: It would be very helpful to include an example of using an existing Weaviate database, which included inspecting the DB schema to determine the values to use for `index_name` and `text_key`.

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I_kwDOIPDwls58Bx0m

KeyError: 'intermediate_steps' when using Wikipedia tool within the create_react_agent function

closed

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ``` from langchain import hub from langchain.agents import create_react_agent from langchain.tools import Tool from langchain_community.utilities.wikipedia import WikipediaAPIWrapper from langchain_community.llms.openai import OpenAI instructions = """You are an agent designed to write and execute python code to answer questions. You have access to Wikipedia, which you can use to search informations. Be smart and select the correct wikipedia for a specific search when several are available. """ base_prompt = hub.pull("langchain-ai/react-agent-template") prompt = base_prompt.partial(instructions=instructions) wiki = WikipediaAPIWrapper() wiki_tool = Tool( name="wikipedia", description="A wikipedia tool that allow to search in it", func=wiki.run, ) agent = create_react_agent(OpenAI(name="gpt-3.5-turbo", temperature=0), tools=[wiki_tool], prompt=prompt) print(agent.invoke({"input":"What is Nostradamus know for"}) ) ``` ### Description I was trying to use the Wikipedia tool in a ReAct agent. I thought it will run smoothly but a KeyError intermediate_step trigged ### System Info langchain 0.1.0 langchain-community 0.0.12 langchain-core 0.1.10 langchain-experimental 0.0.49 langchainhub 0.1.14 langsmith 0.0.80 wikipedia 1.4.0 ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kBqnJ

community: implement adelete from VectorStore in Qdrant

closed

CONTRIBUTOR

**Description:** Implement `adelete` function from `VectorStore` in `Qdrant` to support other asynchronous flows such as async indexing (`aindex`) which requires `adelete` to be implemented. Since `Qdrant` can be passed an async qdrant client, this can be supported easily. <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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I_kwDOIPDwls58BkMq

Status is Pending Forever while using a websocket connection (py & FastAPI)

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code using python and FastAPI ``` async def websocket_handler(websocket: WebSocket, chain, ws_name="Websocket"): await websocket.accept() try: json_payload = await websocket.receive_json() payload = Payload.parse_obj(json_payload) async with atrace_as_chain_group(ws_name, run_id=uuid.uuid4(), tags=[f"store_id:{payload.metadata.store_id}"]) as group_manager: res = await chain.arun(dialog=payload.dialog, page_sku=payload.metadata.sku, extracted_properties=payload.metadata.extracted_properties, callbacks=group_manager, websocket=websocket) await websocket.send_json(res.json()) await websocket.close() except WebSocketDisconnect: print(f"WebSocket connection - {ws_name} - Closed") @app.websocket("/chat") async def chat(websocket: WebSocket): await websocket_handler(websocket, router_chain, ws_name="Chat") @app.post("/quick_suggestions") async def quick_suggestions(payload: Payload): print(f"Quick Suggestions: {payload}") async with atrace_as_chain_group("QuickSuggestions", run_id=uuid.uuid4(), tags=[f"store_id:{payload.metadata.store_id}"]) as group_manager: return await quick_suggestions_chain.arun(dialog=payload.dialog, page_sku=payload.metadata.sku, callbacks=group_manager) ``` When calling to chat route that is using websocket connection it stays in a pending status forever no matter what. When calling QuickSuggestions it behave as expected. I guess its something related to the websocket connection. Any ideas? ### Description when using a websocket connection it stays in a pending status forever no matter what ### System Info [[source]] url = "https://pypi.org/simple" verify_ssl = true name = "pypi" [packages] langchain = ">=0.0.257" openai = "*" pydantic = "==1.10.11" requests = "*" python-dotenv = "*" pandas = "*" pydantic-sqlalchemy = "*" sentence-transformers = "*" google-cloud-logging = "*" asyncpg = "*" fastapi = "*" starlette = "*" uvicorn = "*" sqlalchemy = {extras = ["asyncio"], version = "*"} asyncio = "*" elasticsearch = {extras = ["async"], version = "*"} faiss-cpu = "*" nltk = "*" async-lru = "*" posthog = "*" websockets = "*" spacy = "*" [dev-packages] scikit-learn = "*" gradio = "*" [requires] python_version = "3.10" ### 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

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PR_kwDOIPDwls5kBn_d

langchain: Add konlpy based text splitting for Korean

open

CONTRIBUTOR

- **Description:** Adds a text splitter based on [Konlpy](https://konlpy.org/en/latest/#start) which is a Python package for natural language processing (NLP) of the Korean language. (It is like Spacy or NLTK for Korean) - **Dependencies:** Konlpy would have to be installed before this splitter is used, - **Twitter handle:** @untilhamza

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I_kwDOIPDwls58BOmf

LLM cache includes `azure_ad_token` in caching key which may frequently change across identical requests.

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code None ### Description * I am using the `AzureChatOpenAI` integration combined with the `SQLiteCache`. * I noticed that the cache was working as intended within a single script invocation but not across multiple script invocations. * I looked at what was being used as the cache key by inspecting what was being passed to the `SQLAlchemyCache.update()` method in the `llm_string` argument. Included is the `azure_ad_token` field used to initialise the `AzureChatOpenAI` class: ``` {"lc": 1, "type": "constructor", "id": ["langchain", "chat_models", "azure_openai", "AzureChatOpenAI"], "kwargs": {"azure_ad_token": "<removed>", "azure_endpoint": "<removed>", "openai_api_version": "2023-09-01-preview", "model": "gpt-4-0613", "temperature": 0.0, "max_tokens": 2000, "openai_api_type": "azure"}}---[('stop', **None)]** ``` * Our workflow involves rotating the `azure_ad_token` for every script invocation which is invalidating any cache matches. The `azure_ad_token` field does not have any effect on the model output and is thus not needed and should not be included in the caching key. ### System Info N/A ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kBXNS

partner: Add license field to partner library pyproject.toml files

closed

NONE

- **Description:** This PR adds a license field to the pyproject.toml files of the langchain partner libraries (langchain-openai etc.). - **Issue:** #15998 - **Dependencies:** Not applicable

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I_kwDOIPDwls58BMpT

Langchain partner libraries (langchain-openai etc.) missing license field pyproject.toml files

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code Not applicable ### Description The pyproject.toml files of the langchain partner libraries (langchain-openai etc.) are missing a license field. This prevents acceptance with some automated package scanning software. ### System Info Not applicable ### 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

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I_kwDOIPDwls58Aqc9

The `dict` method is deprecated; use `model_dump` instead [pydantic]

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code The followiwng simple example will log the warning: ```python from langchain_openai import AzureChatOpenAI from langchain.schema import HumanMessage chat_llm = AzureChatOpenAI() chat_llm.invoke([HumanMessage(content='howdy!')]) ``` ### Description I'm seeing a few warning logs saying that langchain-openai is using a deprecated method from pydantic: `dict` ```txt .venv/Lib/site-packages/langchain_openai/chat_models/azure.py:199: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/ response = response.dict() tests/test_zllm_completion.py::test_prompt .venv/Lib/site-packages/langchain_openai/llms/base.py:344: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/ response = response.dict() ``` Here: https://github.com/langchain-ai/langchain/blob/7b084b4cc768a8e5cddc29bfdfb6e5dc1003a5d9/libs/partners/openai/langchain_openai/chat_models/azure.py#L199 And here: https://github.com/langchain-ai/langchain/blob/7b084b4cc768a8e5cddc29bfdfb6e5dc1003a5d9/libs/partners/openai/langchain_openai/llms/base.py#L344 (There might me other cases) ### System Info langchain==0.1.0 Microsoft Windows 11 Pro Python 3.11.3 ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5kA5_L

[community][vectorstores][SurrealDBStore] Fix to match SurrealDB 0.3.2 SDK

closed

CONTRIBUTOR

New version of SurrealDB python sdk was causing the integration to break. This fix addresses that change.

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PR_kwDOIPDwls5kA504

[community][document loaders] Added document loader for SurrealDB

closed

CONTRIBUTOR

Added a simple document loader to work with SurrealDB.

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PR_kwDOIPDwls5kA1P1

docs: fix typo in inspect runnables docs

closed

CONTRIBUTOR

- **Description:** Fixing a typo related to prompts in the inspecting runnables docs

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I_kwDOIPDwls58AcvY

[Feature Request] Cloudflare AI Gateway

open

NONE

### Feature request Support Cloudflare AI Gateway's endpoint `https://gateway.ai.cloudflare.com/v1/ACCOUNT_TAG/GATEWAY` See https://developers.cloudflare.com/ai-gateway/providers/universal/ ### Motivation CF Workers AI (https://developers.cloudflare.com/workers-ai/get-started/rest-api/) is currently supported via the `CloudflareWorkersAI`, Cloudflare AI Gateway is a different product with a different API (`CloudflareWorkersAI` with a custom `endpoint_url` won't work), and is currently unsupported. ### Your contribution Yes I can make a PR.

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PR_kwDOIPDwls5kAdAW

community: Add PolygonLastQuote Tool and Toolkit

open

CONTRIBUTOR

**Description:** In this PR, I am adding a `PolygonLastQuote` Tool, which can be used to get the latest price quote for a given ticker / stock. Additionally, I've added a Polygon Toolkit, which we can use to encapsulate future tools that we build for Polygon. **Twitter handle:** [@virattt](https://twitter.com/virattt)

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PR_kwDOIPDwls5kAHsZ

Use newer MetadataVectorCassandraTable in Cassandra vector store

closed

CONTRIBUTOR

as VectorTable is deprecated Tested manually with `test_cassandra.py` vector store integration test.

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PR_kwDOIPDwls5kAC4_

community : Fixed 'coroutine' object is not subscriptable error

closed

CONTRIBUTOR

- **Description:** Added parenthesis in return statement of aembed_query() funtion to fix 'coroutine' object is not subscriptable error. - **Dependencies:** NA

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PR_kwDOIPDwls5j_0ny

Update chatglm.py

open

NONE

Currently, chatglm3 has modified its interface, and the previous version cannot be used normally. Therefore, relevant modifications have been made based on the latest situation.

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I_kwDOIPDwls57_Cfl

Add the 'id' attribute to the Document class.

open

NONE

### Feature request Add the 'id' attribute to the Document class. Or add a method to get id. ### Motivation Adding an 'id' attribute makes data manipulation easier. When using the Weaviate database, I need to manually set additional parameters to query the 'id', and it's not convenient to use. ### Your contribution At the moment, there isn't one. I'd like to discuss this issue first.

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I_kwDOIPDwls57_Bcc

not able to use local llm endpoints in chains

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code qa_chain = ( RunnablePassthrough.assign(context=( lambda x: format_docs(x["context"]))) | rag_prompt | local_llm_api() | StrOutputParser() ) ### Description I am trying to call my local api endpoint in the qa chain. The return type of the endpoint is a dict of list. The error i am getting is : Expected a Runnable, callable or dict.Instead got an unsupported type: <class 'list'> ### System Info python version - 3.11 ### 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

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PR_kwDOIPDwls5j_aVT

integration Test CI fix

closed

CONTRIBUTOR

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PR_kwDOIPDwls5j_S_o

langchain[minor] - more generic streaming support

open

COLLABORATOR

This PR introduces astream_event interface to all runnables. Checklist: - [x] Verify that each event has a good schema - [x] Verify that each event does not do unintuitive type coercsions (inputs/outputs should not be automatically upgraded to dicts). This needs to be addressed `inputs=inputs if isinstance(inputs, dict) else {"input": inputs}` - [ ] ~~Event `data` should not be dict if it doesn't need to be, but we don't want to get this wrong in case we need to dump extra info there!~~ `data` will always be a dict, with optional keys of `input` / `output` / `chunk` depending on event type. The reason is that `input` is not always available at event start - [x] We need to make sure that the root run also generates event information - [x] Propagate inputs when possible in Runnables - [x] Tool inputs are encoded as json strings? Need to decode potentially - [ ] Documentation Changes downstream: - `inputs` available in `logs` path of astream_log -- this could lead to breaking changes in langserve if langserve fails to serialize any of the messages. (If there are messages there that are well known but haven't been registered as well known messages). - the `output` and `final_output` has been made more consistent, one could argue that this is a bug fix, but if it's widely used could cause user's code to break. Potential Future Changes: * StreamingEvent add `parent_id` key which has the id of the parent runnable * [Internal] Update the BaseTracer or create a new version of it that stops doing type coercions (needs to be backwards compatibility to avoid breaking existing functionality that relies on dictinoaries)

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PR_kwDOIPDwls5j_N2O

langchain: Add konlpy based text splitter for Korean text

closed

CONTRIBUTOR

- **Description:** Add a text splitter based on [Konlpy](https://konlpy.org/en/latest/#start) which is a Python package for natural language processing (NLP) of the Korean language. (It is like Spacy or NLTK for Korean) - **Issue:** the issue # it fixes if applicable, - **Dependencies:** Konlpy would have to be installed before this splitter is used, - **Twitter handle:** @untilhamza If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17

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PR_kwDOIPDwls5j-9nQ

docs(community): update Amadeus toolkit to langchain v0.1

closed

CONTRIBUTOR

- **Description:** docs update following the changes introduced in #15879 <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5j-8m3

Make openai extra dependency import langchain-openai

open

CONTRIBUTOR

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PR_kwDOIPDwls5j-xvz

community[patch]: core min 0.1.9

closed

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PR_kwDOIPDwls5j-wAg

community[patch]: release 0.0.12

closed

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PR_kwDOIPDwls5j-nZa

infra: add nvidia api secret to integration testing

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PR_kwDOIPDwls5j-m2A

community: Add PolygonAPIWrapper and get_last_quote endpoint

closed

CONTRIBUTOR

- **Description:** Added a `PolygonAPIWrapper` and an initial `get_last_quote` endpoint, which allows us to get the last price quote for a given `ticker`. Once merged, I can add a Polygon tool in `tools/` for agents to use. - **Twitter handle:** [@virattt](https://twitter.com/virattt) The Polygon.io Stocks API provides REST endpoints that let you query the latest market data from all US stock exchanges.

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PR_kwDOIPDwls5j-kdT

Fix bug in login functionality

closed

NONE

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I_kwDOIPDwls579tUO

Bug in langchain_community/embeddings/huggingface_hub.py HuggingFaceHubEmbeddings.embed_query

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code from langchain_community.embeddings import HuggingFaceHubEmbeddings url = "https://svvwc5yh51gt1pp3.us-east-1.aws.endpoints.huggingface.cloud" text = "You do not need a weatherman to know which way the wind blows" token = "<my_token>" embeddings = HuggingFaceHubEmbeddings( model = url, huggingfacehub_api_token=token ) qembed = embeddings.embed_query(text) ### Description The code above should call my HuggingFace inference endpoint and generate an embedding. Instead, it throws: Traceback (most recent call last): File "/Users/jraby/python/hfhub/callhub2.py", line 29, in <module> qembed = embeddings.embed_query(text) File "/opt/homebrew/lib/python3.10/site-packages/langchain_community/embeddings/huggingface_hub.py", line 109, in embed_query response = self.embed_documents([text])[0] KeyError: 0 The "[0]" at the end of line 109 is incorrect. self.embed_documents returns JSON, -not a list. The first node in the JSON is 'embeddings', and it contains the list of embedding arrays. I believe that this line: response = self.embed_documents([text])[0] Should be replaced with this: response = self.embed_documents([text])['embeddings'][0] This resolves the problem for me. ### System Info Platform = MacOS python version = 3.10 pip3 huggingface_hub (0.20.2) ### Related Components - [ ] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5j-bkJ

core[patch]: simple prompt pretty printing

closed

COLLABORATOR

Nothing fancy. No support for partials, few shot prompts, multimodal prompts yet. Can add later if people request. 

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PR_kwDOIPDwls5j-NoN

community: Added new Utility runnables for NVIDIA Riva.

open

NONE

**Please tag this issue with `nvidia_genai`** - **Description:** Added new Runnables for integration NVIDIA Riva into LCEL chains for Automatic Speech Recognition (ASR) and Text To Speech (TTS). - **Issue:** N/A - **Dependencies:** To use these runnables, the NVIDIA Riva client libraries are required. It they are not installed, an error will be raised instructing how to install them. The Runnables can be safely imported without the riva client libraries. - **Twitter handle:** N/A All of the Riva Runnables are inside a single folder in the Utilities module. In this folder are four files: - common.py - Contains all code that is common to both TTS and ASR - stream.py - Contains a class representing an audio stream that allows the end user to put data into the stream like a queue. - asr.py - Contains the RivaASR runnable - tts.py - Contains the RivaTTS runnable The following Python function is an example of creating a chain that makes use of both of these Runnables: ```python def create( config: Configuration, audio_encoding: RivaAudioEncoding, sample_rate: int, audio_channels: int = 1, ) -> Runnable[ASRInputType, TTSOutputType]: """Create a new instance of the chain.""" _LOGGER.info("Instantiating the chain.") # create the riva asr client riva_asr = RivaASR( url=str(config.riva_asr.service.url), ssl_cert=config.riva_asr.service.ssl_cert, encoding=audio_encoding, audio_channel_count=audio_channels, sample_rate_hertz=sample_rate, profanity_filter=config.riva_asr.profanity_filter, enable_automatic_punctuation=config.riva_asr.enable_automatic_punctuation, language_code=config.riva_asr.language_code, ) # create the prompt template prompt = PromptTemplate.from_template("{user_input}") # model = ChatOpenAI() model = ChatNVIDIA(model="mixtral_8x7b") # type: ignore # create the riva tts client riva_tts = RivaTTS( url=str(config.riva_asr.service.url), ssl_cert=config.riva_asr.service.ssl_cert, output_directory=config.riva_tts.output_directory, language_code=config.riva_tts.language_code, voice_name=config.riva_tts.voice_name, ) # construct and return the chain return {"user_input": riva_asr} | prompt | model | riva_tts # type: ignore ``` The following code is an example of creating a new audio stream for Riva: ```python input_stream = AudioStream(maxsize=1000) # Send bytes into the stream for chunk in audio_chunks: await input_stream.aput(chunk) input_stream.close() ``` The following code is an example of how to execute the chain with RivaASR and RivaTTS ```python output_stream = asyncio.Queue() while not input_stream.complete: async for chunk in chain.astream(input_stream): output_stream.put(chunk) ``` Everything should be async safe and thread safe. Audio data can be put into the input stream while the chain is running without interruptions.

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I_kwDOIPDwls578311

DOC: Quickstart

open

NONE

### Issue with current documentation: I wasn't able to follow the [quick start guide](https://python.langchain.com/docs/get_started/quickstart) because of a dependency issue. Everything was going well until I tried to install Faiss. ### Idea or request for content: My experience trying to follow langchain quick start guide: # The background I'm a experienced developer, but I'm new to python and notebooks. I've been studying langchain for a while now, and I'm trying to get started with the quick start guide. I'm using a mac (Apple M2) and I'm trying to follow the guide using the jupyter notebook on VS code. # The beginning Starting with the first step, I'm trying to install the dependencies. I'm using the following command: ``` pyenv install 3.12.0 # install python version pyenv local 3.12.0 # set python version python -m venv venv # create virtual environment source ./venv/bin/activate # activate virtual environment pip3 install -r requirements.txt # install dependencies ``` Where the requirements.txt file contains the following: ``` jupyterlab langchain==0.1.0 langchain-openai beautifulsoup4 ``` So far so good. Actually, I was so surprised and happy that everything worked so well, that I kind of knew something was going to go wrong. Anyways... I was able to install everything without any problems, and with that run the notebook from beginning to the beginning of "Retrieval Chain". # The problem When I try to [install Faiss](https://python.langchain.com/docs/get_started/quickstart#retrieval-chain): ``` pip install faiss-cpu ``` I get the following a [error](https://github.com/facebookresearch/faiss/issues/2868#issuecomment-1889724871) (which apparently is not only me who is getting it). Not only that, but it seems Faiss is not [supposed to be installed using `pip`](https://github.com/facebookresearch/faiss/issues/2991#issuecomment-1667640023). After trying `conda` and a bunch of other things, none of which worked, I decided to try a different local vector db. # The kafkaesque journey So the next step was trying to install any local vector db that could work and hopefully be compatible with the quick start guide. **Chroma**: Added `chromadb` to the requirements.txt file and tried to install it. Got the following error: ```bash ... ERROR: Cannot install -r requirements.txt (line 1), -r requirements.txt (line 3) and -r requirements.txt (line 5) because these package versions have conflicting dependencies. The conflict is caused by: chromadb 0.4.22 depends on onnxruntime>=1.14.1 chromadb 0.4.21 depends on onnxruntime>=1.14.1 chromadb 0.4.20 depends on onnxruntime>=1.14.1 chromadb 0.4.19 depends on onnxruntime>=1.14.1 chromadb 0.4.18 depends on onnxruntime>=1.14.1 chromadb 0.4.17 depends on onnxruntime>=1.14.1 chromadb 0.4.16 depends on onnxruntime>=1.14.1 chromadb 0.4.15 depends on onnxruntime>=1.14.1 chromadb 0.4.14 depends on onnxruntime>=1.14.1 chromadb 0.4.13 depends on onnxruntime>=1.14.1 chromadb 0.4.12 depends on onnxruntime>=1.14.1 chromadb 0.4.11 depends on onnxruntime>=1.14.1 chromadb 0.4.10 depends on onnxruntime>=1.14.1 chromadb 0.4.9 depends on onnxruntime>=1.14.1 chromadb 0.4.8 depends on onnxruntime>=1.14.1 chromadb 0.4.7 depends on onnxruntime>=1.14.1 chromadb 0.4.6 depends on onnxruntime>=1.14.1 chromadb 0.4.5 depends on onnxruntime>=1.14.1 chromadb 0.4.4 depends on onnxruntime>=1.14.1 chromadb 0.4.3 depends on onnxruntime>=1.14.1 chromadb 0.4.2 depends on onnxruntime>=1.14.1 chromadb 0.4.1 depends on onnxruntime>=1.14.1 chromadb 0.4.0 depends on onnxruntime>=1.14.1 chromadb 0.3.29 depends on onnxruntime>=1.14.1 chromadb 0.3.27 depends on onnxruntime>=1.14.1 chromadb 0.3.26 depends on onnxruntime>=1.14.1 chromadb 0.3.25 depends on onnxruntime>=1.14.1 langchain 0.1.0 depends on numpy<2 and >=1 langchain-openai 0.0.2 depends on numpy<2 and >=1 chromadb 0.3.2 depends on numpy~=1.21.6 langchain 0.1.0 depends on numpy<2 and >=1 langchain-openai 0.0.2 depends on numpy<2 and >=1 chromadb 0.3.1 depends on numpy~=1.21.6 langchain 0.1.0 depends on numpy<2 and >=1 langchain-openai 0.0.2 depends on numpy<2 and >=1 chromadb 0.3.0 depends on numpy~=1.21.6 langchain 0.1.0 depends on numpy<2 and >=1 langchain-openai 0.0.2 depends on numpy<2 and >=1 chromadb 0.2.0 depends on numpy~=1.21.6 langchain 0.1.0 depends on numpy<2 and >=1 langchain-openai 0.0.2 depends on numpy<2 and >=1 chromadb 0.1.0 depends on numpy~=1.21.6 To fix this you could try to: 1. loosen the range of package versions you've specified 2. remove package versions to allow pip attempt to solve the dependency conflict ``` As far as I can tell, and after reading a few posts, it seems that there is not much I can do about this. So I tried the next one. **Milvus**: Added `pymilvus` to the requirements.txt file and tried to install it. Got the following error: ``` distutils.errors.DistutilsExecError: command '/usr/bin/clang' failed with exit code 1 During handling of the above exception, another exception occurred: Traceback (most recent call last): ... ... File "/private/var/folders/tl/nm6kkwtd3nvdnxbqy2l70c5c0000gn/T/pip-install-e9hhztoq/grpcio_1166b67744e74d958d0c1fd853565676/src/python/grpcio/commands.py", line 293, in new_compile return old_compile( ^^^^^^^^^^^^ File "/private/var/folders/tl/nm6kkwtd3nvdnxbqy2l70c5c0000gn/T/pip-build-env-3zwys1bz/overlay/lib/python3.12/site-packages/setuptools/_distutils/unixccompiler.py", line 187, in _compile raise CompileError(msg) distutils.errors.CompileError: command '/usr/bin/clang' failed with exit code 1 [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for grpcio Failed to build grpcio ERROR: Could not build wheels for grpcio, which is required to install pyproject.toml-based projects ``` I tried a few stackoverflow solutions, but none of them worked. Blocked again. # The solution I haven't found a solution yet, but I would like to share my experience so far. I will keep trying to find a solution, and if I do, I will update this post. However, I would like to enfatize how painful has been trying to start using langchain for someone who is new to python and the hole ecosystem. I know it's a new field and in a way I felt like configuring the video driver on my first linux distro in the 90s. Sharing this experience is not meant to be a rant, but to help the community to improve the experience for new users.

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PR_kwDOIPDwls5j9U0z

docs: agent table fix

closed

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PR_kwDOIPDwls5j9Uid

rfc: tool executor

closed

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PR_kwDOIPDwls5j9I5m

multiple[patch]: .post releases and pyproject metadata

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PR_kwDOIPDwls5j8_Ks

openai[patch]: 0.0.2.post1, urls

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I_kwDOIPDwls578HYU

Runnable error

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code qa_chain = ( RunnablePassthrough.assign(context=( lambda x: format_docs(x["context"]))) | rag_prompt | custom_model_api(query) | StrOutputParser() ) ### Description I am getting the below error Expected a Runnable, callable or dict.Instead got an unsupported type: <class 'list'> ### System Info python version 3.10 ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async

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I_kwDOIPDwls577xSb

generate_queries results in list with empty inbetween generated questions.

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code multiquery_llm_retriever = MultiQueryRetriever.from_llm( retriever = vectordb.as_retriever(), llm=llm, include_original = top_k_params["mq_include_original"] ) docs = multiquery_llm_retriever.get_relevant_documents(query=user_input) Response of the underlying generate_queries function: {'question': 'The actual question', 'text': LineList(lines=['Alternative Question 1: First generated question?', '', 'Alternative Question 2: Second generated question?', '', 'Alternative Question 3: Third generated question?'])} Notice that in between are empty strings: '' They later cause an error in similarity search since empty strings cannot be embedded. Consequently, the retrieve_documents function fails. ### Description I am trying to use the MultiQueryRetriever. However, the generate queries function returns a list that contains empty strings in between the 3 (as in the default prompt) generated answers. This will later cause the retrieve_documents to fail since the similarity search throws an exception. ### System Info langchain==0.0.348 langchain-community==0.0.2 langchain-core==0.1.0 Python 3.11.6 ### 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

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PR_kwDOIPDwls5j8qNb

community: fix the "page" mode in the AzureAIDocumentIntelligenceParser (bug)

closed

CONTRIBUTOR

**Description**: the "page" mode in the AzureAIDocumentIntelligenceParser is not accessible due to a wrong membership test. The mode argument can only be a string (also see the assertion in the `__init__`: `assert self.mode in ["single", "page", "object", "markdown"]`, so the check `elif self.mode == ["page"]:` always fails. As a result, effectively the "object" mode is used when selecting the "page" mode, which may lead to errors. The docstring of the `AzureAIDocumentIntelligenceLoader` also ommitted the `mode` parameter alltogether, so I added it. **Issue**: I could not find a related issue (this class is only 3 weeks old anyways) **Dependencies**: this PR does not introduce or affect dependencies. The current demo notebook and examples are not affected because they all use the default markdown mode.

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PR_kwDOIPDwls5j8p0X

docs: add agent prompt creation examples

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I_kwDOIPDwls577ACq

Input or output validation errors are not reported to run manager

open

NONE

### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code ```python prompt_template = ("Human: Say hello to the user using the given username." "Username: {username}") chain = LLMChain( llm=llm, prompt=PromptTemplate(template=prompt_template, input_variables=[]), verbose=True, ) def execute(args:dict): with callbacks.collect_runs() as cb: try: output = chain.invoke(args) print(f"output: {output}") finally: print(f"traced_runs: {cb.traced_runs}") # executes successfully execute({"username": "John"}) # failed with validation error, but traced runs is empty, while it should have one run with exception details execute({"user_name": "John"}) `` ### Description Callback manager doesn't get invoked if there's an input or output validation error. For logging purposes the callback manager should be invoked and any chain related error, even if it's a validation error. The related logic exists in file `langchain/chains/base.py` in `acall` and `__call__` functions, as the `callback_manager` & `run_manager` gets initialized after validating input. To fix this, `run_manager` should be initialized first, then wrap preparing input, output and executing in try/except and update the `run_manager` accordingly ### System Info langchain==0.0.354 langchain-community==0.0.11 langchain-core==0.1.9 Python 3.10.13 ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async

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PR_kwDOIPDwls5j70YY

Add documentation for AstraDBStore

closed

CONTRIBUTOR

Preview: https://langchain-git-fork-cbornet-astradb-store-doc-langchain.vercel.app/docs/integrations/stores/astradb

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PR_kwDOIPDwls5j7q95

Add documentation for AstraDBStore

closed

CONTRIBUTOR

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I_kwDOIPDwls576ikh

1 validation error for LlamaCpp __root__ Could not load Llama model from path

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### Checked other resources - [X] I added a very descriptive title to this issue. - [X] I searched the LangChain documentation with the integrated search. - [X] I used the GitHub search to find a similar question and didn't find it. ### Example Code I run this in google colab and get error: ``` !pip3 install langchain==0.1.0 !CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip3 install llama-cpp-python==0.2.28 !pip3 install pypdf==3.17.4 !pip3 install gdown==4.7.0 !pip3 install sentence-transformers==2.2.2 !pip3 install chromadb==0.4.18 !wget https://huggingface.co/TheBloke/neural-chat-7B-v3-3-GGUF/resolve/main/neural-chat-7b-v3-3.Q4_K_M.gguf from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.llms import LlamaCpp n_gpu_layers = 2 # Metal set to 1 is enough. n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip. callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) # Make sure the model path is correct for your system! llm = LlamaCpp( model_path='/content/neural-chat-7b-v3-3.Q4_K_M.gguf', n_gpu_layers=n_gpu_layers, n_batch=n_batch, n_ctx=4096, f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls callback_manager=callback_manager, verbose=False, ) ``` ### Description I am trying using LlamaCpp with `from langchain.llms import LlamaCpp`. I run the previous code and I get this error: ``` --------------------------------------------------------------------------- ValidationError Traceback (most recent call last) [<ipython-input-4-a01cc1428fc3>](https://localhost:8080/#) in <cell line: 6>() 4 5 # Make sure the model path is correct for your system! ----> 6 llm = LlamaCpp( 7 model_path='/content/Merak-7B-v4-model-q4_k_m.gguf', 8 n_gpu_layers=n_gpu_layers, 1 frames /usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__() ValidationError: 1 validation error for LlamaCpp __root__ Could not load Llama model from path: '/content/neural-chat-7b-v3-3.Q4_K_M.gguf'. Received error fileno (type=value_error) ``` instead of it successfully loaded. ### System Info ``` !apt-get update !apt-get install neofetch -y !neofetch ``` This is the result ``` root@182e8520dc9b ----------------- OS: Ubuntu 22.04.3 LTS x86_64 Host: Google Compute Engine Kernel: 6.1.58+ Uptime: 14 mins Packages: 1301 (dpkg) Shell: bash 5.1.16 Terminal: jupyter-noteboo CPU: Intel Xeon (2) @ 2.299GHz GPU: NVIDIA Tesla T4 Memory: 827MiB / 12978MiB ``` ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [x] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async

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