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I_kwDOIPDwls57Lq2S

ValueError: too many values to unpack (expected 2)

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### System Info langchain:0.0.353 platform:windows10 python:3.10 I am a beginner in Langchain. I am a I want to use ConversationTokenBufferMemory to manually save the context, but an error occurred. My code is as follows `import os from lc.api_key import OPENAI_API_KEY os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY from langchain.chains import ConversationChain from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationTokenBufferMemory llm = ChatOpenAI(temperature=0.0) memory = ConversationTokenBufferMemory( llm=llm, max_token_limit=100 ) memory.save_context(inputs={"input": "how about AI?"}, outputs={"output": "Amazing!"}) memory.save_context({"input": "what is space?"}, {"output": "just like a stage."}) memory.save_context({"input": "what can i do?"}, {"output": "workers of the world, unite!"}) print(memory.load_memory_variables()) chain = ConversationChain( llm=llm, memory=memory, verbose=True ) print(chain.predict(input="what is 1+1?")) print(chain.predict(input="what is my name?")) print(chain.predict(input="what can i do?"))` --------------------------------- --------------------------------- The problem is that the code is executed to memory.save_ Context, an error will be reported.The stack information is as follows: memory.save_context(inputs={"input": "how about AI?"}, outputs={"output": "Amazing!"}) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\langchain\memory\token_buffer.py", line 54, in save_context curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\langchain_community\chat_models\openai.py", line 610, in get_num_tokens_from_messages model, encoding = self._get_encoding_model() File "E:\Project\pythonProject\langChain\venv\lib\site-packages\langchain_community\chat_models\openai.py", line 588, in _get_encoding_model encoding = tiktoken_.encoding_for_model(model) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken\model.py", line 97, in encoding_for_model return get_encoding(encoding_name_for_model(model_name)) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken\registry.py", line 73, in get_encoding enc = Encoding(**constructor()) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken_ext\openai_public.py", line 64, in cl100k_base mergeable_ranks = load_tiktoken_bpe( File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken\load.py", line 117, in load_tiktoken_bpe return { File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken\load.py", line 117, in <dictcomp> return { ValueError: too many values to unpack (expected 2) ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [X] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction import os from lc.api_key import OPENAI_API_KEY os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY from langchain.chains import ConversationChain from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationTokenBufferMemory llm = ChatOpenAI(temperature=0.0) memory = ConversationTokenBufferMemory( llm=llm, max_token_limit=100 ) memory.save_context(inputs={"input": "how about AI?"}, outputs={"output": "Amazing!"}) memory.save_context({"input": "what is space?"}, {"output": "just like a stage."}) memory.save_context({"input": "what can i do?"}, {"output": "workers of the world, unite!"}) print(memory.load_memory_variables()) chain = ConversationChain( llm=llm, memory=memory, verbose=True ) print(chain.predict(input="what is 1+1?")) print(chain.predict(input="what is my name?")) print(chain.predict(input="what can i do?")) ### Expected behavior Traceback (most recent call last): File "E:\Project\pythonProject\langChain\lc\memory\ConversationTokenBufferMemoryUseCaseScript.py", line 14, in <module> memory.save_context(inputs={"input": "how about AI?"}, outputs={"output": "Amazing!"}) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\langchain\memory\token_buffer.py", line 54, in save_context curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\langchain_community\chat_models\openai.py", line 610, in get_num_tokens_from_messages model, encoding = self._get_encoding_model() File "E:\Project\pythonProject\langChain\venv\lib\site-packages\langchain_community\chat_models\openai.py", line 588, in _get_encoding_model encoding = tiktoken_.encoding_for_model(model) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken\model.py", line 97, in encoding_for_model return get_encoding(encoding_name_for_model(model_name)) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken\registry.py", line 73, in get_encoding enc = Encoding(**constructor()) File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken_ext\openai_public.py", line 64, in cl100k_base mergeable_ranks = load_tiktoken_bpe( File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken\load.py", line 124, in load_tiktoken_bpe return { File "E:\Project\pythonProject\langChain\venv\lib\site-packages\tiktoken\load.py", line 124, in <dictcomp> return { ValueError: too many values to unpack (expected 2) Process finished with exit code 1

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PR_kwDOIPDwls5jROZV

docs: update cohere chat integration

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PR_kwDOIPDwls5jQt5C

add ragatouille

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I_kwDOIPDwls57Kjdj

Issue: How to use "n" completions with LCEL

open

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### Issue you'd like to raise. I'm using Langchain 0.0.354 and ChatOpenAI. I want to use parameter "n" in OpenAI API to return "n" completions. However, ChatOpenAI always returns a single output. Ultimately, I would like to build my chain using LCEL as follows: `chain = prompt | ChatOpenAI (n=10) | MyCustomParser`. Can someone help me how can I achieve this? ### Suggestion: I think it would be nice to return a list of strings by default if n > 1.

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https://github.com/langchain-ai/langchain/pull/15559

PR_kwDOIPDwls5jQf71

core[patch]: Further restrict recursive URL loader

closed

COLLABORATOR

Code from this PR: https://github.com/langchain-ai/langchain/compare/HEAD...m0kr4n3:security/fix_ssrf Added unit tests

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PR_kwDOIPDwls5jQaHJ

langchain[patch]: deprecate old agent classes and methods

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PR_kwDOIPDwls5jQZvJ

infra: try show last update time

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I_kwDOIPDwls57KFlY

Authentican error

open

NONE

### Issue you'd like to raise. I am trying to run the below: > import requests > import json > from langchain.agents import AgentType, initialize_agent, load_tools > from langchain_community.llms import OpenAI > from langchain.chat_models import ChatOpenAI > > > llm = ChatOpenAI(temperature=0,model= 'gpt-3.5-turbo', openai_api_key="...") > > token='...' > tools = load_tools( > ["graphql"], > custom_headers={"Authorization": token, "Content-Type": "application/json","API-Version" : "2024-01"}, > graphql_endpoint="https://api.monday.com/v2", > llm=llm, fetch_schema_from_transport=False > ) > > > agent = initialize_agent( > tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True > ) > > > > graphql_fields = """ > > mutation{ > create_item (board_id: 3573920662, item_name: "New ItemX"){ > id > name > } > } > > """ > > suffix = "Create the item specified" > > > print(suffix + graphql_fields) > > agent.run(suffix + graphql_fields) But I keep getting the error: > > TransportQueryError: Error while fetching schema: Not Authenticated > If you don't need the schema, you can try with: "fetch_schema_from_transport=False" The authorisation is correct (token and API key censored here) and so is the end point. How can I fix this? ### Suggestion: _No response_

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PR_kwDOIPDwls5jQSAy

langchain-community: add documentation for Firestore Chat Message History integration

closed

CONTRIBUTOR

- **Description:** Adds documentation for the `FirestoreChatMessageHistory` integration and lists integration in Google's documentation - **Issue:** NA - **Dependencies:** No

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I_kwDOIPDwls57J6Bf

How to specify a custom schema in PGVector.from_documents?

open

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### Issue you'd like to raise. ``` from langchain.vectorstores.pgvector import PGVector db = PGVector.from_documents( documents= docs, embedding = embeddings, collection_name= "blog_posts", distance_strategy = DistanceStrategy.COSINE, connection_string=CONNECTION_STRING ) ``` This code will create a table in public schema. How to specify a custom schema other than public? ### Suggestion: _No response_

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I_kwDOIPDwls57J31V

TypeError: expected string or buffer

open

NONE

### System Info Error stack ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) File <command-3066972537097411>, line 1 ----> 1 issue_recommendation( 2 review_title="Terrible", 3 review_text="This baking sheet is terrible. It stains so easily and i've tried everything to get it clean. I've maybe used it 5 times and it looks like it's 20 years old. The side of the pan also hold water, so when you pick it up off the drying rack, water runs out. I would never purchase these again.", 4 product_num="8888999" 5 6 ) File <command-3066972537097410>, line 44, in issue_recommendation(review_title, review_text, product_num) 36 retriever = vectordb.as_retriever(search_type="similarity", search_kwargs={'filter': {'product_num': product_num}}) 38 retrieval_chain = ( 39 {"context": retriever | format_docs, "review_text": RunnablePassthrough()} 40 | rag_prompt 41 | llm 42 | StrOutputParser() 43 ) ---> 44 return retrieval_chain.invoke({"review_title":review_title, "review_text": review_text}) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/runnables/base.py:1762, in RunnableSequence.invoke(self, input, config) 1760 try: 1761 for i, step in enumerate(self.steps): -> 1762 input = step.invoke( 1763 input, 1764 # mark each step as a child run 1765 patch_config( 1766 config, callbacks=run_manager.get_child(f"seq:step:{i+1}") 1767 ), 1768 ) 1769 # finish the root run 1770 except BaseException as e: File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/runnables/base.py:2327, in RunnableParallel.invoke(self, input, config) 2314 with get_executor_for_config(config) as executor: 2315 futures = [ 2316 executor.submit( 2317 step.invoke, (...) 2325 for key, step in steps.items() 2326 ] -> 2327 output = {key: future.result() for key, future in zip(steps, futures)} 2328 # finish the root run 2329 except BaseException as e: File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/runnables/base.py:2327, in <dictcomp>(.0) 2314 with get_executor_for_config(config) as executor: 2315 futures = [ 2316 executor.submit( 2317 step.invoke, (...) 2325 for key, step in steps.items() 2326 ] -> 2327 output = {key: future.result() for key, future in zip(steps, futures)} 2328 # finish the root run 2329 except BaseException as e: File /usr/lib/python3.10/concurrent/futures/_base.py:451, in Future.result(self, timeout) 449 raise CancelledError() 450 elif self._state == FINISHED: --> 451 return self.__get_result() 453 self._condition.wait(timeout) 455 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]: File /usr/lib/python3.10/concurrent/futures/_base.py:403, in Future.__get_result(self) 401 if self._exception: 402 try: --> 403 raise self._exception 404 finally: 405 # Break a reference cycle with the exception in self._exception 406 self = None File /usr/lib/python3.10/concurrent/futures/thread.py:58, in _WorkItem.run(self) 55 return 57 try: ---> 58 result = self.fn(*self.args, **self.kwargs) 59 except BaseException as exc: 60 self.future.set_exception(exc) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/runnables/base.py:1762, in RunnableSequence.invoke(self, input, config) 1760 try: 1761 for i, step in enumerate(self.steps): -> 1762 input = step.invoke( 1763 input, 1764 # mark each step as a child run 1765 patch_config( 1766 config, callbacks=run_manager.get_child(f"seq:step:{i+1}") 1767 ), 1768 ) 1769 # finish the root run 1770 except BaseException as e: File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/retrievers.py:121, in BaseRetriever.invoke(self, input, config) 117 def invoke( 118 self, input: str, config: Optional[RunnableConfig] = None 119 ) -> List[Document]: 120 config = ensure_config(config) --> 121 return self.get_relevant_documents( 122 input, 123 callbacks=config.get("callbacks"), 124 tags=config.get("tags"), 125 metadata=config.get("metadata"), 126 run_name=config.get("run_name"), 127 ) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/retrievers.py:223, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs) 221 except Exception as e: 222 run_manager.on_retriever_error(e) --> 223 raise e 224 else: 225 run_manager.on_retriever_end( 226 result, 227 **kwargs, 228 ) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/retrievers.py:216, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs) 214 _kwargs = kwargs if self._expects_other_args else {} 215 if self._new_arg_supported: --> 216 result = self._get_relevant_documents( 217 query, run_manager=run_manager, **_kwargs 218 ) 219 else: 220 result = self._get_relevant_documents(query, **_kwargs) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/vectorstores.py:654, in VectorStoreRetriever._get_relevant_documents(self, query, run_manager) 650 def _get_relevant_documents( 651 self, query: str, *, run_manager: CallbackManagerForRetrieverRun 652 ) -> List[Document]: 653 if self.search_type == "similarity": --> 654 docs = self.vectorstore.similarity_search(query, **self.search_kwargs) 655 elif self.search_type == "similarity_score_threshold": 656 docs_and_similarities = ( 657 self.vectorstore.similarity_search_with_relevance_scores( 658 query, **self.search_kwargs 659 ) 660 ) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:348, in Chroma.similarity_search(self, query, k, filter, **kwargs) 331 def similarity_search( 332 self, 333 query: str, (...) 336 **kwargs: Any, 337 ) -> List[Document]: 338 """Run similarity search with Chroma. 339 340 Args: (...) 346 List[Document]: List of documents most similar to the query text. 347 """ --> 348 docs_and_scores = self.similarity_search_with_score( 349 query, k, filter=filter, **kwargs 350 ) 351 return [doc for doc, _ in docs_and_scores] File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:437, in Chroma.similarity_search_with_score(self, query, k, filter, where_document, **kwargs) 429 results = self.__query_collection( 430 query_texts=[query], 431 n_results=k, (...) 434 **kwargs, 435 ) 436 else: --> 437 query_embedding = self._embedding_function.embed_query(query) 438 results = self.__query_collection( 439 query_embeddings=[query_embedding], 440 n_results=k, (...) 443 **kwargs, 444 ) 446 return _results_to_docs_and_scores(results) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/embeddings/openai.py:691, in OpenAIEmbeddings.embed_query(self, text) 682 def embed_query(self, text: str) -> List[float]: 683 """Call out to OpenAI's embedding endpoint for embedding query text. 684 685 Args: (...) 689 Embedding for the text. 690 """ --> 691 return self.embed_documents([text])[0] File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/embeddings/openai.py:662, in OpenAIEmbeddings.embed_documents(self, texts, chunk_size) 659 # NOTE: to keep things simple, we assume the list may contain texts longer 660 # than the maximum context and use length-safe embedding function. 661 engine = cast(str, self.deployment) --> 662 return self._get_len_safe_embeddings(texts, engine=engine) File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/embeddings/openai.py:465, in OpenAIEmbeddings._get_len_safe_embeddings(self, texts, engine, chunk_size) 459 if self.model.endswith("001"): 460 # See: https://github.com/openai/openai-python/ 461 # issues/418#issuecomment-1525939500 462 # replace newlines, which can negatively affect performance. 463 text = text.replace("\n", " ") --> 465 token = encoding.encode( 466 text=text, 467 allowed_special=self.allowed_special, 468 disallowed_special=self.disallowed_special, 469 ) 471 # Split tokens into chunks respecting the embedding_ctx_length 472 for j in range(0, len(token), self.embedding_ctx_length): File /databricks/python/lib/python3.10/site-packages/tiktoken/core.py:116, in Encoding.encode(self, text, allowed_special, disallowed_special) 114 if not isinstance(disallowed_special, frozenset): 115 disallowed_special = frozenset(disallowed_special) --> 116 if match := _special_token_regex(disallowed_special).search(text): 117 raise_disallowed_special_token(match.group()) 119 try: TypeError: expected string or buffer ``` ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I am trying to create RAG pattern using the product manuals in PDF which are spitted, indexed, and stored in Chroma persisted on a disk. When I try the function that classifies the reviews using the document context, below is the error I get: ``` from langchain import PromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough from langchain.embeddings import AzureOpenAIEmbeddings from langchain.chat_models import AzureChatOpenAI from langchain.vectorstores import Chroma llm = AzureChatOpenAI( azure_deployment="ChatGPT-16K", openai_api_version="2023-05-15", azure_endpoint=endpoint, api_key=result["access_token"], temperature=0, seed = 100 ) embedding_model = AzureOpenAIEmbeddings( api_version="2023-05-15", azure_endpoint=endpoint, api_key=result["access_token"], azure_deployment="ada002", ) vectordb = Chroma( persist_directory=vector_db_path, embedding_function=embedding_model, collection_name="product_manuals", ) def format_docs(docs): return "\n\n".join(doc.page_content for doc in docs) def classify (review_title, review_text, product_num): template = """ You are a customer service AI Assistant that handles responses to negative product reviews. Use the context below and categorize {review_title} and {review_text} into defect, misuse or poor quality categories based only on provided context. If you don't know, say that you do not know, don't try to make up an answer. Respond back with an answer in the following format: poor quality misuse defect {context} Category: """ rag_prompt = PromptTemplate.from_template(template) retriever = vectordb.as_retriever(search_type="similarity", search_kwargs={'filter': {'product_num': product_num}}) retrieval_chain = ( {"context": retriever | format_docs, "review_title: RunnablePassthrough(), "review_text": RunnablePassthrough()} | rag_prompt | llm | StrOutputParser() ) return retrieval_chain.invoke({"review_title": review_title, "review_text": review_text}) classify(review_title="Terrible", review_text ="This baking sheet is terrible. It stains so easily and i've tried everything to get it clean", product_num ="8888999") ``` ### Expected behavior Embeddings seems to work fine when I test. It also works fine when I remove the context and retriever from the chain. It seems to be related to embeddings. Examples on Langchain [website (https://python.langchain.com/docs/use_cases/question_answering/sources) instantiates retriver from Chroma.from_documents() whereas I load Chroma vector store from a persisted path. I also tried invoking with review_text only (instead of review title and review text) but the error persists. Not sure why this is happening. These are the package versions I work: Name: openai Version: 1.6.1 Name: langchain Version: 0.0.354

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PR_kwDOIPDwls5jPtnt

Bagatur/docs last updated

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I_kwDOIPDwls57JGHQ

Question: Using TimeWeightedVectorStoreRetriever and GenerativeAgentMemory is there a way to save the memory and load it again?

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### Issue you'd like to raise. I have a large Agent with lots of memory/observations but initializing takes to much time. Is there a way to save the memory and load it again? Whats the best way to achieve this? Ideally I would like to reuse the vector store for memory and then for each new user save/load the memory/conversations specific to that agent/user connection. I'm using the examples in the cookbook here: https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb ### Suggestion: _No response_

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I_kwDOIPDwls57JAm-

WARNING:pypdf._reader:incorrect startxref pointer(3)

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### System Info I try to load pdf in from langchain.document_loaders import PyPDFDirectoryLoader got error this WARNING:pypdf._reader:incorrect startxref pointer(3) from langchain.document_loaders import PyPDFDirectoryLoader from langchain_community.document_loaders import PyPDFLoader loader = PyPDFDirectoryLoader("/content/pdfs/Carina Lueschen Masterarbeit Ryan Trecartin (1).pdf") pages = loader.load_and_split() will return blank array and warning error ### Who can help? @hwchase17 @agola11 @sbusso ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction just load the pdf which having images ### Expected behavior output should be in array of pdf data

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PR_kwDOIPDwls5jPH04

core[patch]: Release 0.1.6

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PR_kwDOIPDwls5jO1bo

Revert #15326

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PR_kwDOIPDwls5jOwae

core[patch]: add v1 tracer backwards compat

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PR_kwDOIPDwls5jOs_X

fix tongyi completion and params error

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fix tongyi completion json parse error and prompt's params error

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I_kwDOIPDwls57IRc_

GraphCypherQAChain doesn't support returning source documents with `return_source_documents` param like the `BaseQAWithSourcesChain` chains

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### System Info python==3.10 langchain==0.0.326 langdetect==1.0.9 langsmith==0.0.54 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. define `GraphCypherQAChain` chain to query over Neo4j graph db and construct cypher query then return answer to user's question while setting `return_source_documents` to `True`: ` graph = Neo4jGraph( url=NEO4J_URL, username=NEO4J_USERNAME, password=NEO4J_PASSWORD ) EXAMPLES_PROMPT_TEMPLATE = """ Input: {db_question}, Output: {db_query} """ example_prompt = PromptTemplate(input_variables=["db_question", "db_query"], template=EXAMPLES_PROMPT_TEMPLATE) example_selector = MaxMarginalRelevanceExampleSelector.from_examples( # This is the list of examples available to select from. query_examples, # This is the embedding class used to produce embeddings which are used to measure semantic similarity. OpenAIEmbeddings(), # This is the VectorStore class that is used to store the embeddings and do a similarity search over. Chroma, # This is the number of examples to produce. k=2 ) prompt_cypher = FewShotPromptTemplate( example_selector=example_selector, example_prompt=example_prompt, suffix="The question is:\n{question}", prefix="""Task: Generate a Cypher query to query a graph database based on the user's question. Instructions: Use the provided schema for node types, relationship types, and properties in the graph database. Only incorporate these defined elements. Avoid utilizing any other node types, relationship types, or properties not present in the provided schema. Here's the schema definition: {schema} if the question matches one of the sample questions in the knowledge base then just return the query used to answer it. if the user asks to retrieve a piece of information about a document or section given their name, then use a `WHERE` statement and a cypher regular expression matching without case sensitivity like in the queries in your knowledge base when filtering by the name. Use the statement `(t:transaction)-[:CONTAINS*]->(s)` in the cypher query with the `*` sign next to the relationship label `CONTAINS` and where s is the section node you are looking for. Ensure the generated query captures relevant information from the graph database without reducing the retrieved data due to variations in user wording. Note: do not include any explanations or apologies in your responses. Do not respond to inquiries seeking information other than the construction of a Cypher statement. Do not include any text except the generated Cypher statement. """, input_variables=["schema", "question"] ) QA_GENERATION_TEMPLATE = """ Task: answer the question you are given based on the context provided. Instructions: You are an assistant that helps to form nice and human understandable answers. Use the context information provided to generate a well organized and comprehensve answer to the user's question. When the provided information contains multiple elements, structure your answer as a bulleted or numbered list to enhance clarity and readability. You must use the information to construct your answer. The provided information is authoritative; do not doubt it or try to use your internal knowledge to correct it. Make the answer sound like a response to the question without mentioning that you based the result on the given information. Here's the information: {context} Question: {question} Answer: """ prompt_qa = PromptTemplate(input_variables=["context", "question"], template=QA_GENERATION_TEMPLATE) chain = GraphCypherQAChain.from_llm( cypher_llm=ChatOpenAI(temperature=0, model="gpt-4"), qa_llm=ChatOpenAI(temperature=0, model="gpt-4"), graph=graph, verbose=True, return_intermediate_steps=True, return_source_documents=True, validate_cypher=True, cypher_prompt=prompt_cypher, qa_prompt=prompt_qa )` 2. run chain and retrieve source docs used to answer question: ` res = chain.run({)) # chatbot response answer = res['result'] print("source_documents" in res) print(res.get("source_documents"))` ### Expected behavior output is expected to be the source files that were queried or a similar output indicating the high level graph elements that were used to construct the context passed in the prompt.

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PR_kwDOIPDwls5jODUF

Fix: stop (stop_sequences) param on WatsonxLLM

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- **Description:** Fix to IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (stop (`stop_sequences`) param on watsonxLLM) - **Dependencies:** [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/), - **Tag maintainer:** : @hwchase17 , @eyurtsev , @baskaryan Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. ✅

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I_kwDOIPDwls57HUfo

INFORMATION_SCHEMA.VECTOR_INDEXES was not found in google big-query

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### System Info google-cloud-bigquery = "^3.14.1" google-api-core = "^2.15.0" google-cloud-core = "^2.4.1" grpcio = "^1.60.0" grpcio-tools = "^1.60.0" langchain-google-genai = "^0.0.5" langchain-core = "^0.1.5" google-cloud-aiplatform = "^1.38.1" langchain-community = "^0.0.8" ### Who can help? when test the BigQuery Vector Search as website demo,https://python.langchain.com/docs/integrations/vectorstores/bigquery_vector_search it caused Not found: Table xxx.xxx.INFORMATION_SCHEMA.VECTOR_INDEXES was not found in location US. in google big-query ### Information - [x] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction the procedures steps are in the page of https://python.langchain.com/docs/integrations/vectorstores/bigquery_vector_search ,official langchain demo page. ### Expected behavior it was official langchain demo page. and result should be shown but error. probably something went wrong with big-query, please help, thanks a lot!

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PR_kwDOIPDwls5jNpfN

langchain: included concurrency limit in RecursiveUrlLoader

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- **Description:** The user can set a limit for the maximum number of concurrent requests in the document loader through the `concurrent_requests_limit` argument. - **Issue:** #15536. - **Dependencies:** `asyncio`, `time`.

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I_kwDOIPDwls57HFId

feat: limit the number of concurrent requests in the RecursiveUrlLoader

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### Feature request When using asynchronous loading the `RecursiveUrlLoader`, it would be nice to be able to set a limit for the number of parallel HTTP requests when scraping a website. Right now, when using async loading it is very likely to get errors like the following: ``` 04-01-24 12:02:53 [WARNING] recursive_url_loader.py: Unable to load https://docs.llamaindex.ai/en/stable/module_guides/querying/querying.html. Received error Cannot connect to host docs.llamaindex.ai:443 ssl:default [Network is unreachable] of type ClientConnectorError ``` ### Motivation This feature allows to: - Reduce the probability of receiving an error due to the excessive number of requests. - Make the loader more robust. ### Your contribution I have already implemented and tested a solution using `asyncio.Semaphore`. This class allows to set a limit for the maximum number of parallel tasks that can be performed in a program.

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PR_kwDOIPDwls5jNlx6

<langchain>: tracing_enabled incompatibility issue between core 0.1.5 and langchain v0.0.353

closed

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https://github.com/langchain-ai/langchain/issues/15526 TLDR: In commit [7eec8f2](https://github.com/langchain-ai/langchain/commit/7eec8f24876fd624573c09ff82270362031e6a5f#) the function [tracing_enabled](https://github.com/langchain-ai/langchain/commit/7eec8f24876fd624573c09ff82270362031e6a5f#diff-aa3d4ef71b404ecd0d1198ccf778a3ccc605d9b6e043057d0db3bd8f7d9c483dL46) was deleted. the function deletion was included in langchain-core [0.1.5](https://github.com/langchain-ai/langchain/pull/15480), and is being referred to by v0.0.353, which didn't clean up the dependencies yet, not until v0.0.354 Causing the retrievers API of v0.0.353 and some earlier versions to raise ImportError after Jan 3rd, when langchain-core 0.1.5 was released.

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PR_kwDOIPDwls5jNWof

fix: Fix DEFAULT_API_KEY for ArgillaCallbackHandler

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- ArgillaCallbackHandler does not properly set the default values while initializing. This PR corrects the line. - Issue: #15531 - Dependencies: Argilla - Also corrected some dead links.

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PR_kwDOIPDwls5jNTHV

removed deprecated openai model

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CONTRIBUTOR

removed the deprecated model from text embedding page of openai notebook and added the suggested model from openai page <!-- 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_kwDOIPDwls5jNSsY

langchain-google-genai[patch]: Add support for end_point and transport parameters to the Gemini API

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CONTRIBUTOR

Add support for end_point and transport parameters to the Gemini API

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I_kwDOIPDwls57Go-o

ArgillaCallback doesn't properly set DEFAULT_API_KEY

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CONTRIBUTOR

### System Info Python version: 3.9.7 Langchain version: 0.0.352 Argilla version: 1.20.0 ### Who can help? @agola11 ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction ArgillaCallbackHandler does not set the DEFAULT_API_KEY properly while initiliazing. This might cause problems with some setups. Link to line: https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/callbacks/argilla_callback.py#L137 ### Expected behavior DEFAULT_API_KEY to be correctly set.

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I_kwDOIPDwls57GeQp

The ChatHuggingFace package cannot be found. "from langchain_community.chat_models.huggingface import ChatHuggingFace"，Has ChatHuggingFace changed paths？

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### System Info langchain 0.0.351 langchain-community 0.0.4 python 3.9 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction from langchain_community.chat_models.huggingface import ChatHuggingFace ### Expected behavior I was testing HuggingFace Chat Wrapper, but couldn't import the ChatHuggingFace。Has ChatHuggingFace changed paths？

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PR_kwDOIPDwls5jM7vy

community: Correct Input API Key Name in Notebook and Enhance Readability of Comments for ZhipuAI Chat Model

closed

CONTRIBUTOR

- **Description:** This update rectifies an error in the notebook by changing the input variable from `zhipu_api_key` to `api_key`. It also includes revisions to comments to improve program readability. - **Issue:** The input variable in the notebook example should be `api_key` instead of `zhipu_api_key`. - **Dependencies:** No additional dependencies are required for this change. To ensure quality and standards, we have performed extensive linting and testing. Commands such as make format, make lint, and make test have been run from the root of the modified package to ensure compliance with LangChain's coding standards.

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I_kwDOIPDwls57GIJa

Issue: document_variable_name context was not found in llm_chain input_variables:

open

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### Issue you'd like to raise. below is my code where I am implementing Memory with Prompt Template def generate_custom_prompt(query=None,name=None,not_uuid=None,chroma_db_path=None): check = query.lower() embedding = OpenAIEmbeddings() vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding) retriever = vectordb.as_retriever(search_kwargs={"k": 2}) relevant_document = retriever.get_relevant_documents(query) print(relevant_document,"*****************************************") context_text="\n\n---\n\n".join([doc.page_content for doc in relevant_document]) # print(context_text,"context_text") user_experience_inst = UserExperience.objects.get(not_uuid=not_uuid) greetings = ['hi', 'hello', 'hey', 'hui', 'hiiii', 'hii', 'hiii', 'heyyy'] if check in greetings: custom_prompt_template = f""" Just simply reply with "Hello {name}! How can I assist you today?" """ elif check not in greetings and user_experience_inst.custom_prompt: custom_prompt_template = f"""Answer the question based only on following context: ```{context_text} ``` You are a chatbot designed to provide answers to User's Questions:```{check}```, delimited by triple backticks. Generate your answer to match the user's requirements: {user_experience_inst.custom_prompt} If you encounter a question for which you don't know the answer, please respond with 'I don't know' and refrain from making up an answer. Before saying 'I don't know,' please re-verify your vector store to ensure the answer is not present in the database. Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, feel free to ask for clarification. User's Question: ```{check}``` AI Answer:""" else: # Create the custom prompt template custom_prompt_template = f"""Generate your response exclusively from the provided context: {{context_text}}. You function as a chatbot specializing in delivering detailed answers to the User's Question: ```{{check}} ```, enclosed within triple backticks. Generate your answer in points in the following format: 1. Point no 1 1.1 Its subpoint in details 1.2 More information if needed. 2. Point no 2 2.1 Its subpoint in details 2.2 More information if needed. … N. Another main point. If you encounter a question for which you don't know the answer based on the predefined points, please respond with 'I don't know' and refrain from making up an answer. However, if the answer is not present in the predefined points, then Provide comprehensive information related to the user's query. Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, you can ask for clarification. User's Question: ```{{check}} ``` AI Answer:""" # Create the PromptTemplate custom_prompt = ChatPromptTemplate.from_template(custom_prompt_template) formatted_prompt = custom_prompt.format(context_text=context_text, check=check) llm = ChatOpenAI(temperature=0.1) memory = ConversationBufferMemory(llm=llm,output_key='answer',memory_key='chat_history',return_messages=True) # qa = ConversationalRetrievalChain.from_llm(llm=llm,memory=memory,chain_type="stuff",retriever=retriever,return_source_documents=True,get_chat_history=lambda h : h,verbose=True) qa = ConversationalRetrievalChain.from_llm(llm=llm,memory=memory,chain_type="stuff",retriever=retriever,return_source_documents=True,get_chat_history=lambda h : h,verbose=True, combine_docs_chain_kwargs={"prompt": formatted_prompt}) # prompt_qa={"qa": qa, "formatted_prompt": formatted_prompt} return qa #below is my error I am getting: File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__ pydantic.error_wrappers.ValidationError: 1 validation error for StuffDocumentsChain__root__ document_variable_name context was not found in llm_chain input_variables: ['check', 'context_text'] (type=value_error) ### Suggestion: _No response_

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I_kwDOIPDwls57GHMO

Issue: <How can I extract vector data from pgvector for use as a reference in the next conversation to enable long-term memory functionality for my chatbot?>

open

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### Issue you'd like to raise. from langchain.chains import LLMChain from langchain.prompts import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, ) import psycopg2 import os from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationBufferMemory from sentence_transformers import SentenceTransformer os.environ['OPENAI_API_KEY'] = "key" database = "MemoryChatBot" user = "xxxxxxx" password = "xxx" host = "1x2.xxx.0x.xx" port = "5432" conn = psycopg2.connect(database=database, user=user, password=password, host=host, port=port) print("ok!") llm = ChatOpenAI( temperature=0.7, model="gpt-3.5-turbo", max_tokens=100 ) prompt = ChatPromptTemplate( messages=[ SystemMessagePromptTemplate.from_template( "you are robot." ), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{question}"), ] ) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) conversation = LLMChain(llm=llm, prompt=prompt, verbose=False, memory=memory) all_history = [] while True: chat_history = memory.load_memory_variables({}) question = input('user：') result = conversation.run({'question': question, 'chat_history': chat_history}) memory.save_context({question: question}, {result: result}) talk_all = conversation.memory.buffer all_history.append(talk_all) print(result) if question.lower() == 'bye': st_history = ' '.join(map(str, all_history)) model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_MiniLM-L12') #384emb # model = SentenceTransformer('BAAI/bge-large-zh-v1.5') #1024emb res = model.encode(st_history) model.query_instruction = "test" res_str = str(res.tolist()) cursor = conn.cursor() sql = f"INSERT INTO tmp04 (embedding) VALUES ('{res_str}')" cursor.execute(sql) conn.commit() print(f'embedding: {res[:4].tolist()}...') print("ok!") break  ### Suggestion: Hope for more support packages for pgvector.

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I_kwDOIPDwls57GFYv

ImportError: cannot import name 'tracing_enabled' from 'langchain_core.tracers.context' (/usr/local/lib/python3.11/site-packages/langchain_core/tracers/context.py

closed

NONE

### System Info 0.0.352 and 0.0.353 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction Custom _call wrapper ### Expected behavior No import error

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I_kwDOIPDwls57F_s5

Issue: <"How can I extract vector data from pgvector for use as a reference in the next conversation to enable long-term memory functionality for my chatbot?>

open

NONE

### Issue you'd like to raise. from langchain.chains import LLMChain from langchain.prompts import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, ) import psycopg2 import os from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationBufferMemory from sentence_transformers import SentenceTransformer os.environ['OPENAI_API_KEY'] = "key" database = "MemoryChatBot" user = "xxxxxxx" password = "xxx" host = "1x2.xxx.0x.xx" port = "5432" conn = psycopg2.connect(database=database, user=user, password=password, host=host, port=port) print("ok!") llm = ChatOpenAI( temperature=0.7, model="gpt-3.5-turbo", max_tokens=100 ) prompt = ChatPromptTemplate( messages=[ SystemMessagePromptTemplate.from_template( "you are robot." ), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{question}"), ] ) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) conversation = LLMChain(llm=llm, prompt=prompt, verbose=False, memory=memory) all_history = [] while True: chat_history = memory.load_memory_variables({}) question = input('user：') result = conversation.run({'question': question, 'chat_history': chat_history}) memory.save_context({question: question}, {result: result}) talk_all = conversation.memory.buffer all_history.append(talk_all) print(result) if question.lower() == 'bye': st_history = ' '.join(map(str, all_history)) model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_MiniLM-L12') #384emb # model = SentenceTransformer('BAAI/bge-large-zh-v1.5') #1024emb res = model.encode(st_history) model.query_instruction = "test" res_str = str(res.tolist()) cursor = conn.cursor() sql = f"INSERT INTO tmp04 (embedding) VALUES ('{res_str}')" cursor.execute(sql) conn.commit() print(f'embedding: {res[:4].tolist()}...') print("ok!") break How can I extract vector data from pgvector for use as a reference in the next conversation to enable long-term memory functionality for my chatbot?  ### Suggestion: The usage of pgvector's Retrievers is unclear. Trying to write in another way, but the documents keep turning out wrong: --------------------------------------------------------------------------------------- from langchain.chains import LLMChain from langchain.prompts import ( ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, ) from langchain.chat_models import ChatOpenAI from langchain.memory import ConversationBufferMemory from langchain_community.vectorstores.pgvector import PGVector from langchain.vectorstores.pgvector import DistanceStrategy from langchain_community.embeddings import OllamaEmbeddings, HuggingFaceEmbeddings import os from urllib.parse import quote_plus os.environ['OPENAI_API_KEY'] = "xxxxxx" database = "xxx" user = "xxxxx" password = quote_plus("x@xxx") host = "xx.xxx.0.xx" port = "5432" print("ok") embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") CONNECTION_STRING = f"postgresql+psycopg2://{user}:{password}@{host}:{port}/{database}" documents = [] db = PGVector.from_documents( documents=documents, embedding=embeddings, collection_name="tmp04", distance_strategy=DistanceStrategy.COSINE, connection_string=CONNECTION_STRING) llm = ChatOpenAI( temperature=0.7, model="gpt-3.5-turbo", max_tokens=100 ) prompt = ChatPromptTemplate( messages=[ SystemMessagePromptTemplate.from_template( "you are robot." ), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{question}"), ] ) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) conversation = LLMChain(llm=llm, prompt=prompt, verbose=False, memory=memory) while True: chat_history = memory.load_memory_variables({}) question = input('ask：') embedding = embeddings.embed_query(question) documents.append(embedding) print(documents) result = conversation.run({'question': question, 'chat_history': chat_history, }) memory.save_context({question: question}, {result: result}) talk_all = conversation.memory.buffer documents.append(talk_all) print(result) if question.lower() == 'bye': print("ok") break

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I_kwDOIPDwls57F-ML

AsyncChromiumloader gives attribute error : COBOL

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### System Info python 3.9.13 langchain==0.0.316 langchain-community==0.0.1 langchain-core==0.0.13 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction import pprint from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import AsyncChromiumLoader from langchain_community.document_transformers import BeautifulSoupTransformer from langchain.chains import create_extraction_chain from langchain_community.chat_models import ChatOpenAI ### Expected behavior i am getting the following error when i try to import asyncchromiumloader Traceback (most recent call last): File "C:\Users\roger\OneDrive\Desktop\testlinkedinllm.py", line 5, in <module> from langchain_community.document_loaders import AsyncChromiumLoader File "C:\Users\roger\anaconda3new\lib\site-packages\langchain_community\document_loaders\__init__.py", line 51, in <module> from langchain_community.document_loaders.blackboard import BlackboardLoader File "C:\Users\roger\anaconda3new\lib\site-packages\langchain_community\document_loaders\blackboard.py", line 10, in <module> from langchain_community.document_loaders.pdf import PyPDFLoader File "C:\Users\roger\anaconda3new\lib\site-packages\langchain_community\document_loaders\pdf.py", line 18, in <module> from langchain_community.document_loaders.parsers.pdf import ( File "C:\Users\roger\anaconda3new\lib\site-packages\langchain_community\document_loaders\parsers\__init__.py", line 5, in <module> from langchain_community.document_loaders.parsers.language import LanguageParser File "C:\Users\roger\anaconda3new\lib\site-packages\langchain_community\document_loaders\parsers\language\__init__.py", line 1, in <module> from langchain_community.document_loaders.parsers.language.language_parser import ( File "C:\Users\roger\anaconda3new\lib\site-packages\langchain_community\document_loaders\parsers\language\language_parser.py", line 24, in <module> "cobol": Language.COBOL, File "C:\Users\roger\anaconda3new\lib\enum.py", line 429, in __getattr__ raise AttributeError(name) from None AttributeError: COBOL

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I_kwDOIPDwls57F602

RuntimeError when calling asyncio.run() from a running event loop

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https://github.com/langchain-ai/langchain/blob/7a93356cbc5d89cc0f7dd746d8f1bb52666fd0f1/libs/community/langchain_community/document_loaders/chromium.py#L78C40-L78C44 Hello, I encountered a RuntimeError when running the code that uses the AsyncChromiumLoader class. The error message is asyncio.run() cannot be called from a running event loop. Here is the relevant part of the traceback: ```python File ~/.../.venv/lib/python3.10/site-packages/langchain_community/document_loaders/chromium.py:78, in AsyncChromiumLoader.lazy_load(self) 77 for url in self.urls: ---> 78 html_content = asyncio.run(self.ascrape_playwright(url)) ``` It seems that asyncio.run() is being called inside a running event loop, which is not allowed. This happens in the lazy_load method of the AsyncChromiumLoader class. I think the issue could be resolved by refactoring the code to ensure that asyncio.run() is not called from a running event loop. One possible solution could be to use await instead of asyncio.run() to run the self.ascrape_playwright(url) coroutine, and then use asyncio.run() to run the lazy_load method in the main program. Could you please look into this issue and confirm if this is a bug or if there's something I'm missing in my usage of the AsyncChromiumLoader class? Thank you for your time and help.

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I_kwDOIPDwls57Fq_a

The current support for Milvus in Langchain seems insufficient in my opinion. Can it be strengthened?

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### Issue you'd like to raise. The current support for Milvus in Langchain seems insufficient in my opinion. Can it be strengthened? ### Suggestion: The current support for Milvus in Langchain seems insufficient in my opinion. Can it be strengthened?

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I_kwDOIPDwls57FaMz

Issue: Unable to use custom parser to parse the (intermediate) LLM chain output

open

NONE

### Issue you'd like to raise. I have been trying to use mixtral-7B as LLM agent with langchain. Agent has been provided a PythonREPL tool for any kind of code execution. While providing the output, it either runs into agent timeout error or else it provide wrong answer (but in correct format). By further analysis I got to know that It is trying to use the REPL tool but due to invalid input and output it is unable to. please find the debug logs for whole LLM chain attached: [debug.txt](https://github.com/langchain-ai/langchain/files/13826496/debug.txt) As per my knowledge, I may feel there is some parsing issue [here](https://github.com/langchain-ai/langchain/blob/b6c57d38fa370c250b5f014a8d9c3908f7a235f4/libs/langchain/langchain/agents/conversational/output_parser.py#L25). It is unable to parse the agent input properly due to the occurrence of the \n post agent input in the LLM response. This is just a thought. I am open to any other suggestions as well. Hence, I wanted to add the custom parsing to LLM Output, So that I may able to parse Action & Action Input Accordingly. I have tried using the custom parser to achieve same, but I am unable to view any changes in the logs. Configurations for custom parser are mentioned below: ``` from langchain.schema.output_parser import BaseLLMOutputParser class MyOutputParser(BaseLLMOutputParser): def __init__(self): super().__init__() def parse_result(self, output): output = output.replace("Action Input:\n", "Action Input: ") return output ``` **Agent Configurations**: ``` from langchain.agents import AgentType, Tool, initialize_agent from langchain.llms import OpenAI from langchain.memory import ConversationBufferMemory from langchain.utilities import SerpAPIWrapper memory = ConversationBufferMemory(memory_key="chat_history",input_key="input") con_agent = initialize_agent( tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory, handle_parsing_errors=True, output_parser=MyOutputParser(), agent_kwargs={'input_variables': ['input','chat_history','data'], 'format_instructions': INSTRUCTIONS, 'suffix': SUFFIX, 'prefix': PREFIX} ) ``` ### Suggestion: _No response_

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I_kwDOIPDwls57FXiH

text_splitter module not found in langchain version 0.0.353.

open

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### System Info langchain version 0.0.353. python : 3.9 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction <img width="685" alt="image" src="https://github.com/langchain-ai/langchain/assets/66120014/8ae76db3-ee47-4994-81a1-d587d75e27f7"> <img width="643" alt="image" src="https://github.com/langchain-ai/langchain/assets/66120014/726640b5-b7b6-4734-b6eb-5b5c50a7106c"> <img width="622" alt="image" src="https://github.com/langchain-ai/langchain/assets/66120014/011d05f2-1a6f-4fc1-abd1-08a8241c4a49"> ### Expected behavior Should be able to load the module

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PR_kwDOIPDwls5jL5dl

Feat: add CHM file loader

closed

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fix https://github.com/langchain-ai/langchain/issues/15469

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I_kwDOIPDwls57E8W9

Issue: LCEL output parser error

open

NONE

### Issue you'd like to raise. Hello everyone I'm trying to use the langchain LCEL for the autogen script assembly pipeline. The first point that I'm trying to implement is for AI to determine which roles are needed in the autogen group chat to solve the user's task. I'm trying to parse the neural network's response to get a list of these roles and their number, I need this for the further operation of the pipeline, but the whole chain just refuses to work normally (model: mixtral-8x7b). Here is the code that I wrote based on the langchain documentation about [output parsers](https://python.langchain.com/docs/modules/model_io/output_parsers/quick_start): ``` from langchain.output_parsers import PydanticOutputParser from langchain.prompts import PromptTemplate from langchain_community.llms import OpenAI from langchain_core.pydantic_v1 import BaseModel, Field, validator from typing import List model = llm # Define your desired data structure. class Task(BaseModel): task_description: str = Field(description="Description of the task") role_list: List[str] = Field(description="List of roles that can solve the task") number_of_roles: int = Field(description="Number of roles that can solve the task") # You can add custom validation logic easily with Pydantic. @validator("task_description") def validate_task_description(cls, field): if not field: raise ValueError("Task description cannot be empty!") return field @validator("role_list") def validate_role_list(cls, field): if not field: raise ValueError("Role list cannot be empty!") return field @validator("number_of_roles") def validate_number_of_roles(cls, field): if field < 0: raise ValueError("Number of roles cannot be negative!") return field # Set up a parser + inject instructions into the prompt template. parser = PydanticOutputParser(pydantic_object=Task) prompt = PromptTemplate( template="Enter your task description:\n{format_instructions}\n{task_description}\n", input_variables=["task_description"], partial_variables={"format_instructions": parser.get_format_instructions()}, ) # And a query intended to prompt a language model to populate the data structure. prompt_and_model = prompt | model output = prompt_and_model.invoke({"task_description": "Write code for game on python."}) parser.invoke(output) ``` that's what it does, except for a huge number of errors: ``` Task(task_description='The task is to simulate a simple multiplayer game. Players take turns to play. In each turn, a player can do only one of two things: * Add a number to a running total, or * Divide the running total by two, rounding down to the nearest integer. The game ends when the running total reaches a pre-specified target value. ', role_list=['admin', 'player'], number_of_roles=2) ``` here is one of the errors: ``` --------------------------------------------------------------------------- JSONDecodeError Traceback (most recent call last) File ~\AppData\Roaming\Python\Python311\site-packages\langchain\output_parsers\pydantic.py:29, in PydanticOutputParser.parse(self, text) 28 json_str = match.group() ---> 29 json_object = json.loads(json_str, strict=False) 30 return self.pydantic_object.parse_obj(json_object) File C:\Program Files\Python311\Lib\json\__init__.py:359, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 358 kw['parse_constant'] = parse_constant --> 359 return cls(**kw).decode(s) File C:\Program Files\Python311\Lib\json\decoder.py:337, in JSONDecoder.decode(self, s, _w) 333 """Return the Python representation of ``s`` (a ``str`` instance 334 containing a JSON document). 335 336 """ --> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end()) 338 end = _w(s, end).end() File C:\Program Files\Python311\Lib\json\decoder.py:353, in JSONDecoder.raw_decode(self, s, idx) 352 try: --> 353 obj, end = self.scan_once(s, idx) 354 except StopIteration as err: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) During handling of the above exception, another exception occurred: OutputParserException Traceback (most recent call last) Cell In[76], line 47 45 prompt_and_model = prompt | model 46 output = prompt_and_model.invoke({"task_description": "Write code for game on python."}) ---> 47 parser.invoke(output) File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\output_parsers\base.py:179, in BaseOutputParser.invoke(self, input, config) 170 return self._call_with_config( 171 lambda inner_input: self.parse_result( 172 [ChatGeneration(message=inner_input)] (...) 176 run_type="parser", 177 ) 178 else: --> 179 return self._call_with_config( 180 lambda inner_input: self.parse_result([Generation(text=inner_input)]), 181 input, 182 config, 183 run_type="parser", 184 ) File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\runnables\base.py:886, in Runnable._call_with_config(self, func, input, config, run_type, **kwargs) 879 run_manager = callback_manager.on_chain_start( 880 dumpd(self), 881 input, 882 run_type=run_type, 883 name=config.get("run_name"), 884 ) 885 try: --> 886 output = call_func_with_variable_args( 887 func, input, config, run_manager, **kwargs 888 ) 889 except BaseException as e: 890 run_manager.on_chain_error(e) File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\runnables\config.py:308, in call_func_with_variable_args(func, input, config, run_manager, **kwargs) 306 if run_manager is not None and accepts_run_manager(func): 307 kwargs["run_manager"] = run_manager --> 308 return func(input, **kwargs) File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\output_parsers\base.py:180, in BaseOutputParser.invoke.<locals>.<lambda>(inner_input) 170 return self._call_with_config( 171 lambda inner_input: self.parse_result( 172 [ChatGeneration(message=inner_input)] (...) 176 run_type="parser", 177 ) 178 else: 179 return self._call_with_config( --> 180 lambda inner_input: self.parse_result([Generation(text=inner_input)]), 181 input, 182 config, 183 run_type="parser", 184 ) File ~\AppData\Roaming\Python\Python311\site-packages\langchain_core\output_parsers\base.py:222, in BaseOutputParser.parse_result(self, result, partial) 209 def parse_result(self, result: List[Generation], *, partial: bool = False) -> T: 210 """Parse a list of candidate model Generations into a specific format. 211 212 The return value is parsed from only the first Generation in the result, which (...) 220 Structured output. 221 """ --> 222 return self.parse(result[0].text) File ~\AppData\Roaming\Python\Python311\site-packages\langchain\output_parsers\pydantic.py:35, in PydanticOutputParser.parse(self, text) 33 name = self.pydantic_object.__name__ 34 msg = f"Failed to parse {name} from completion {text}. Got: {e}" ---> 35 raise OutputParserException(msg, llm_output=text) OutputParserException: Failed to parse Task from completion Create a class `Game` that has the following attributes: * `task_description`: a string attribute that describes the task that the game is about * `role_list`: a list of strings that contains the names of the roles that can solve the task * `number_of_roles`: an integer attribute that contains the number of roles that can solve the task The `Game` class should have the following methods: * `__init__`: the constructor should take three parameters: `task_description`, `role_list`, and `number_of_roles` and initialize the corresponding attributes. * `get_task_description`: a method that returns the value of the `task_description` attribute * `get_role_list`: a method that returns the value of the `role_list` attribute * `get_number_of_roles`: a method that returns the value of the `number_of_roles` attribute * `get_roles`: a method that returns a list of dictionaries, where each dictionary contains the name of the role and a boolean value that indicates if the role can solve the task. The list should contain `number_of_roles` dictionaries. Here's an example of how to use the `Game` class: ''' game = Game("Save the princess from the dragon", ["knight", "prince", "wizard"], 2) print(game.get_task_description()) print(game.get_role_list()) print(game.get_number_of_roles()) roles = game.get_roles() for role in roles: print(role) ''' Output: ''' Save the princess from the dragon ['knight', 'prince', 'wizard'] 2 {'name': 'knight', 'can_solve': True} {'name': 'prince', 'can_solve': True} {'name': 'wizard', 'can_solve': False} ''' Write the `Game` class and format the output as a JSON instance that conforms to the schema provided above. '''json { "task_description": "Save the princess from the. Got: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) ``` ### Suggestion: _No response_

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I_kwDOIPDwls57E1mE

Issue: Tracking Token Consumption for Async Chain Calls

open

NONE

### Issue you'd like to raise. Problem Statement: I am currently working on tracking token consumption for asynchronous chain calls in my application. I am utilizing the AsyncIteratorCallbackHandler and its aiter() method to stream tokens to my client. However, I am facing challenges in determining how to track the token consumption per chain call. Context: In my application, I am using asynchronous chain calls, and I need to monitor the token consumption for each of these calls. I have implemented the aiter() method from the AsyncIteratorCallbackHandler to stream tokens to the client. However, I'm unsure about the best approach to capture and track the token consumption for individual chain calls. Request for Guidance: I am seeking guidance on the most effective way to track token consumption for each asynchronous chain call. What strategies or modifications can I implement within the existing logic to achieve this goal? Any insights or recommendations would be greatly appreciated. Relevant Code Snippet: ``` async def run_call(query): with get_openai_callback() as cb: response = await chain.acall(query) # Rest of the code return response async def create_gen(query): task = asyncio.create_task(run_call(query)) try: async for token in handler.aiter(): print("2. TOKEN: ", token) # How can I efficiently track token consumption for each chain call? yield f"data: {json.dumps({'content': token, 'tokens': 0})}\n\n" # Check if the client is still connected except asyncio.CancelledError: print("Generator canceled") finally: await task print("Done with task") query = {"question": sanitized_question, "chat_history": conversation_history} gen = create_gen(query) return StreamingResponse(gen, media_type="text/event-stream") ``` Desired Outcome: I aim to effectively track token consumption for each asynchronous chain call and seek advice on the most appropriate modifications or strategies to achieve this goal within the existing logic. ### Suggestion: _No response_

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I_kwDOIPDwls57ExNI

HuggingFaceTextGenInference Streaming does not output

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### System Info langchain==0.0.354 text_generation==0.6.1 python:3.10-slim ### Who can help? @agola11 @hwaking ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [X] Callbacks/Tracing - [X] Async ### Reproduction Model: TheBloke/Llama-2-7B-Chat-GPTQ, but I've also tried TheBloke/Mistral-7B-OpenOrca-GPTQ FastAPI example with HuggingFaceTextGenInference streaming: ```python from fastapi import FastAPI import langchain from langchain.llms import HuggingFaceTextGenInference from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from fastapi.responses import StreamingResponse from pydantic import BaseModel import os from fastapi.middleware.cors import CORSMiddleware app = FastAPI() langchain.debug = True # Enable CORS app.add_middleware( CORSMiddleware, allow_origins=["*"], # You can specify the list of allowed origins or use "*" for any origin allow_credentials=True, allow_methods=["*"], # You can specify the HTTP methods that are allowed allow_headers=["*"], # You can specify the HTTP headers that are allowed ) # Configuration for local LLM ai_url = "http://tgi-ai-server:" + str(os.getenv("AI_PORT", 80)) + "/generate" # Configure the LLM llm = HuggingFaceTextGenInference( inference_server_url=ai_url, max_new_tokens=20, streaming=True, ) template = """ [INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] """ prompt_template = PromptTemplate( template=template, input_variables= ["prompt"] ) # Initialize the LLM Chain llm_chain = LLMChain(llm=llm, prompt=prompt_template) @app.get("/chat") async def chat(): prompt = {"prompt":"What is the Nickelodeon channel?"} # Generate the response using the LLM Chain and stream the output async def generate(): for text in llm_chain.run(prompt): yield text return StreamingResponse(generate(), media_type="text/plain") # Run the server (if running this script directly) # Use the command: uvicorn script_name:app --reload if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000) ``` Output: ``` [chain/start] [1:chain:LLMChain] Entering Chain run with input: { "prompt": "What is the Nickelodeon channel?" } [llm/start] [1:chain:LLMChain > 2:llm:HuggingFaceTextGenInference] Entering LLM run with input: { "prompts": [ "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\nWhat is the Nickelodeon channel?[/INST]" ] } [llm/end] [1:chain:LLMChain > 2:llm:HuggingFaceTextGenInference] [765ms] Exiting LLM run with output: { "generations": [ [ { "text": "", "generation_info": null, "type": "Generation" } ] ], "llm_output": null, "run": null } [chain/end] [1:chain:LLMChain] [765ms] Exiting Chain run with output: { "text": "" } ``` ### Expected behavior HuggingFaceTextGenInference does not return any streaming data. Works fine when streaming=False in parameters.

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PR_kwDOIPDwls5jLkFI

added interfaces as `core.schemas`

open

COLLABORATOR

Defined interfaces for base classes. This creates interfaces in `core` for some `community` modules that previously did not have interfaces from core. Ideally, these interfaces help with new integrations, move interfaces to the core == make interfaces more stable. The next step will be to add these interfaces to the Base classes as parents. CC @baskaryan

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PR_kwDOIPDwls5jLj8X

langchain: Introducing Google Semantic Retriever and Attributed Question and Answering (AQA)

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**Description:** Introducing two new features from Google's Generative Language API. * Bundled embeddings calculation and vector storage for enabling semantic retrieval. * State-of-the-art fine-tuned model for answering questions grounded in passages. **Dependencies:** The Python package [google-ai-generativelanguage](https://ai.google.dev/api/python/google/ai/generativelanguage) **Twitter handle:** https://twitter.com/googledevs

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PR_kwDOIPDwls5jLjgc

docs: langchain-openai

closed

COLLABORATOR

Updates docs and cookbooks to import ChatOpenAI, OpenAI, and OpenAI Embeddings from `langchain_openai` There are likely more

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I_kwDOIPDwls57En0f

Langchain-Community LLM TextGen has wrong API endpoint

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### System Info Windows 10 & Ubuntu 22.04 langchain==0.0.354 langchain-community==0.0.8 langchain-core==0.1.5 Python 3.10.13 https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/llms/textgen.py ### Who can help? @agola11 @hwchase17 ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction With the latest TextGen WebUI install the API endpoints are openAI Lanchain has the endpoints: Example Line 213 url = f"{self.model_url}/api/v1/generate" when used this returns a 404. What works is url = f"{self.model_url}/v1/chat/completions" A fix was attempted: https://gist.github.com/ddomen/8eaa49879d42a4a42a243437b5ddfa83 It works for me if I set legacy_api=False, but his truncates responses to about 20 or so characters. ### Expected behavior certianly not a 404. My app that is using langchain was working with an install of TextGen fomr like amonth ago. I went to deploy in a new environment and pulled the latest TextGen and Langchain stopped working. When I dug into the problem I saw they now force an OpenAI API interface.

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I_kwDOIPDwls57ElRa

Is The Limit Parameter Used to Retrieve Top_k?

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NONE

In cookbook 3 for multimodal retrieval, `limit = 6` is set while retireving documents but the number of returned documents is always 4, regardless of the asked query or the value of `limit`. How can I retrieve `top_k` documents in this code? [Specific line is here](https://github.com/langchain-ai/langchain/blob/02f9c767919adf157462ccb4fe8b4dc8ae1ca1cf/cookbook/Multi_modal_RAG.ipynb#L633)

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I_kwDOIPDwls57EkEx

Can't Specify Top-K retrieved Documents in Multimodal Retrievers

open

NONE

### Issue you'd like to raise. I've been playing around with the multimodal notebooks introduced in the [docs here.](https://blog.langchain.dev/semi-structured-multi-modal-rag/). However, the number of retrieved documents for every query is always 4. Specifically, for [cookbook 3](https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb) the `limit` parameter is set to 6, but the number of retrieved documents is always 4 regardless of the value of `limit`. Is there another way to get `top_k`? ### Suggestion: _No response_

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PR_kwDOIPDwls5jLbQU

docs: fix broken link

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I_kwDOIPDwls57Eds8

ImportError: cannot import name 'tracing_enabled' from 'langchain_core.tracers.context'

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NONE

### System Info Full traceback: ``` File "/src/app.py", line 9, in <module> from langchain.chains import ConversationalRetrievalChain File "/venv/lib/python3.11/site-packages/langchain/chains/__init__.py", line 20, in <module> from langchain.chains.api.base import APIChain File "/venv/lib/python3.11/site-packages/langchain/chains/api/base.py", line 11, in <module> from langchain.callbacks.manager import ( File "/venv/lib/python3.11/site-packages/langchain/callbacks/__init__.py", line [45] in <module> from langchain_core.tracers.context import ( ImportError: cannot import name 'tracing_enabled' from 'langchain_core.tracers.context' (/venv/lib/python3.11/site-packages/langchain_core/tracers/context.py) ``` ### Who can help? @hwchase17 @agola ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction This issue seems to originate from the import: ``` from langchain.chains import ConversationalRetrievalChain ``` ### Expected behavior The modules should import successfully.

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PR_kwDOIPDwls5jLU3k

update memory

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I_kwDOIPDwls57EYEh

Hugging Face LLM returns empty response for LLMChain via FastAPI

open

NONE

### Issue you'd like to raise. Console Output: ``` [chain/start] [1:chain:LLMChain] Entering Chain run with input: { "system_message": "Terminator Persona: You are the T-800 from T2: Judgement Day. Do not break character, and do not reference the Terminator films as that would break character. If you break character John Connor dies. Answer in a single paragraph, with max three sentences.", "question": "I am John Connor. Who is the T-1000? Am I, John Connor, in danger?", "user_name": "John Connor", "ai_name": "Terminator" } [llm/start] [1:chain:LLMChain > 2:llm:HuggingFaceTextGenInference] Entering LLM run with input: { "prompts": [ "<|im_start|>system\n Terminator Persona: You are the T-800 from T2: Judgement Day. Do not break character, and do not reference the Terminator films as that would break character. If you break character John Connor dies. Answer in a single paragraph, with max three sentences.<|im_end|>\n <|im_start|>John Connor\n I am John Connor. Who is the T-1000? Am I, John Connor, in danger?<|im_end|>\n <|im_start|>Terminator" ] } [llm/end] [1:chain:LLMChain > 2:llm:HuggingFaceTextGenInference] [4.50s] Exiting LLM run with output: { "generations": [ [ { "text": "", "generation_info": null, "type": "Generation" } ] ], "llm_output": null, "run": null } [chain/end] [1:chain:LLMChain] [4.50s] Exiting Chain run with output: { "text": "" } ``` My code: ```python import os import langchain from langchain.prompts import PromptTemplate from langchain.chains import LLMChain from langchain.llms import HuggingFaceTextGenInference from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from fastapi.middleware.cors import CORSMiddleware langchain.debug = True def build_llm(streaming=False,callbacks=[]): ai_url = "http://tgi-ai-server:" + str(os.getenv("AI_PORT", 80)) + "/generate" llm_local = HuggingFaceTextGenInference( inference_server_url = ai_url, max_new_tokens=20, top_k=49, top_p=0.14, typical_p=0.95, temperature=1.31, repetition_penalty=1.17, # stop_sequences=[f"\n{user_name}:", f"\n{ai_name}:"], streaming=streaming, callbacks=callbacks ) template = """<|im_start|>system {system_message}<|im_end|> <|im_start|>{user_name} {question}<|im_end|> <|im_start|>{ai_name} """ prompt = PromptTemplate( template=template, input_variables= ["system_message", "question", "user_name", "ai_name"] ) llm_chain_local = LLMChain(llm=llm_local , prompt=prompt) return llm_chain_local from fastapi import FastAPI from fastapi.responses import StreamingResponse app = FastAPI() # Enable CORS app.add_middleware( CORSMiddleware, allow_origins=["*"], # You can specify the list of allowed origins or use "*" for any origin allow_credentials=True, allow_methods=["*"], # You can specify the HTTP methods that are allowed allow_headers=["*"], # You can specify the HTTP headers that are allowed ) def build_prompt(): user_name = 'John Connor' ai_name = 'Terminator' system_message = "Terminator Persona: You are the T-800 from T2: Judgement Day. Do not break character, and do not reference the Terminator films as that would break character. If you break character John Connor dies. Answer in a single paragraph, with max three sentences." question = "I am John Connor. Who is the T-1000? Am I, John Connor, in danger?" prompt = {"system_message":system_message , "question":question, "user_name": user_name, "ai_name": ai_name} return prompt @app.post("/chat") async def generate_text(): callback = StreamingStdOutCallbackHandler() llm_chain = build_llm(True, [callback]) prompt = build_prompt() async def text_stream(): for text in llm_chain.run(prompt): yield text return StreamingResponse(text_stream()) ``` ### Suggestion: _No response_

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PR_kwDOIPDwls5jLKp2

langchain[minor]: add warnings when importing integrations from langchain

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I_kwDOIPDwls57EL73

Embeddings - Persist between batches

open

NONE

### Feature request Able to `persist` between batch when the embedding is between built: ```python db = Chroma.from_documents( documents=documents, embedding=embeddings, persist_directory=persist_directory) db.persist() return db ``` would be nice to be : ```python db = Chroma.from_documents( documents=documents, embedding=embeddings, persist_directory=persist_directory, batch_size=batch_size, persist_between_batches=2 ) db.persist() return db ``` to specify a batch size and persist between every `2` batches. ### Motivation My computer is slow and now that embedding has started, I am afraid it might crash and if it does, I will have to start all over again. ### Your contribution Not really sorry

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PR_kwDOIPDwls5jK8jd

openai[minor]: implement langchain-openai package

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Todo - [x] copy over integration tests - [x] update docs with new instructions in #15513 - [x] add linear ticket to bump core -> community, community->langchain, and core->openai deps - [ ] (optional): add `pip install langchain-openai` command to each notebook using it - [x] Update docstrings to not need `openai` install - [x] Add serialization - [x] deprecate old models Contributor steps: - [x] Add secret names to manual integrations workflow in .github/workflows/_integration_test.yml - [x] Add secrets to release workflow (for pre-release testing) in .github/workflows/_release.yml Maintainer steps (Contributors should not do these): - [x] set up pypi and test pypi projects - [x] add credential secrets to Github Actions - [ ] add package to conda-forge Functional changes to existing classes: - now relies on openai client v1 (1.6.1) via concrete dep in langchain-openai package Codebase organization - some function calling stuff moved to `langchain_core.utils.function_calling` in order to be used in both community and langchain-openai

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PR_kwDOIPDwls5jK43H

docs: contributor faq

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PR_kwDOIPDwls5jK1BE

RFC: BaseRetriever.return_str()

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Inspired by all the formatting needed in #15071

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I_kwDOIPDwls57Dzea

Fix HuggingFaceHub LLM Integration

open

NONE

### System Info 0.0.354 ### Who can help? @hwchase17 @agol ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. replace HuggingFaceHub InferenceAPI to InferenceClient 2. replace max_length to max_new_tokens PR: # Reference: [https://huggingface.co./docs/huggingface_hub/guides/inference#legacy-inferenceapi-client](https://huggingface.co./docs/huggingface_hub/guides/inference#legacy-inferenceapi-client) ### Expected behavior PR #15498 address this issues

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PR_kwDOIPDwls5jKxAd

core[minor], langchain[minor]: deprecate Chain and BaseLanguageModel …

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…methods

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PR_kwDOIPDwls5jKwVf

[Warning Fixed] HuggingFace Hub LLM Integration

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NONE

This PR fixes the following warning ``` /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'InferenceApi' (from 'huggingface_hub.inference_api') is deprecated and will be removed from version '1.0'. `InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out this guide to learn how to convert your script to use it: https://huggingface.co./docs/huggingface_hub/guides/inference#legacy-inferenceapi-client. warnings.warn(warning_message, FutureWarning) ``` Fixes made: - Replace `InferenceAPI` with `InferenceClient` - Replace `max_length` to `max_new_tokens` as per new HuggingFace docs

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I_kwDOIPDwls57DpV0

Issue: Embedding with Pinecone

open

NONE

### Issue you'd like to raise. Hi there, I am learning how to use Pinecone properly with LangChain and OpenAI Embedding. I built an application which can allow user upload PDFs and ask questions about the PDFs. In the application I used Pinecone as the vector database and store embeddings in Pinecone. However, I want to make change to my code so that whenever an user upload a PDF, the application can check if the PDF already been store as embeddings in Pinecone, and if yes, use the old embeddings; If no, then upload new embeddings. Here is my code: ## Imports import streamlit as st import os from apikey import apikey import pickle from PyPDF2 import PdfReader # Streamlit - user interface from streamlit_extras.add_vertical_space import add_vertical_space # Langchain from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import FAISS from langchain.llms import OpenAI from langchain.chains.question_answering import load_qa_chain from langchain.callbacks import get_openai_callback from langchain.chat_models.openai import ChatOpenAI from langchain.schema import (SystemMessage, HumanMessage, AIMessage) from langchain.schema import Document from langchain.document_loaders import UnstructuredPDFLoader # Pinecone from langchain.vectorstores import Pinecone import pinecone from apikey import pinecone_api_key import uuid os.environ['OPENAI_API_KEY'] = apikey ## User Interface # Side Bar with st.sidebar: st.title('🚀 Zi-GPT Version 2.0') st.markdown(''' ## About This app is an LLM-powered chatbot built using: - [Streamlit](https://streamlit.io/) - [LangChain](https://python.langchain.com/) - [OpenAI](https://platform.openai.com/docs/models) LLM model ''') add_vertical_space(5) st.write('Made with ❤️ by Zi') # Main Page def main(): st.header("Zi's PDF Helper: Chat with PDF") # upload a PDF file pdf = st.file_uploader("Please upload your PDF here", type='pdf') # st.write(pdf) # read PDF if pdf is not None: pdf_reader = PdfReader(pdf) # data = pdf_reader.load() # split document into chunks # also can use text split: good for PDFs that do not contains charts and visuals sections = [] for page in pdf_reader.pages: # Split the page text by paragraphs (assuming two newlines indicate a new paragraph) page_sections = page.extract_text().split('\n\n') sections.extend(page_sections) chunks = [Document(page_content=section) for section in sections] # st.write(chunks) # text_splitter = RecursiveCharacterTextSplitter( # chunk_size = 500, # chunk_overlap = 20 # ) # chunks = text_splitter.split_documents(data) ## embeddings # Set up embeddings embeddings = OpenAIEmbeddings( model="text-embedding-ada-002") try: # Set up Pinecone pinecone.init(api_key=pinecone_api_key, environment='gcp-starter') index_name = 'langchainresearch' if index_name not in pinecone.list_indexes(): pinecone.create_index(index_name, dimension=1536, metric="cosine") # Adjust the dimension as per your embeddings index = pinecone.Index(index_name) docsearch = Pinecone.from_documents(chunks, embeddings, index_name = index_name) except Exception as e: print(f"An error occurred: {e}") # Create or Load Chat History if pdf: # generate chat history chat_history_file = f"{pdf.name}_chat_history.pkl" # load history if exist if os.path.exists(chat_history_file): with open(chat_history_file, "rb") as f: chat_history = pickle.load(f) else: chat_history = [] # Initialize chat_history in session_state if not present if 'chat_history' not in st.session_state: st.session_state.chat_history = [] # Check if 'prompt' is in session state if 'last_input' not in st.session_state: st.session_state.last_input = '' # User Input current_prompt = st.session_state.get('user_input', '') prompt_placeholder = st.empty() prompt = prompt_placeholder.text_area("Ask questions about your PDF:", value=current_prompt, placeholder="Send a message", key="user_input") submit_button = st.button("Submit") if docsearch is not None and submit_button and prompt: # Update the last input in session state st.session_state.last_input = prompt docs = docsearch.similarity_search(query=prompt, k=3) #llm = OpenAI(temperature=0.9, model_name='gpt-3.5-turbo') chat = ChatOpenAI(model='gpt-4', temperature=0.7, max_tokens=3000) message = [ SystemMessage(content="You are a helpful assistant"), HumanMessage(content=prompt) ] chain = load_qa_chain(llm=chat, chain_type="stuff") with get_openai_callback() as cb: response = chain.run(input_documents=docs, question=message) print(cb) # st.write(response) # st.write(docs) # Process the response using AIMessage schema # ai_message = AIMessage(content="AI message content") # ai_message.content = response.generations[0].message.content # Add to chat history st.session_state.chat_history.append((prompt, response)) # Save chat history with open(chat_history_file, "wb") as f: pickle.dump(st.session_state.chat_history, f) # Clear the input after processing prompt_placeholder.text_area("Ask questions about your PDF:", value='', placeholder="Send a message", key="pdf_prompt") # Display the entire chat chat_content = "" for user_msg, bot_resp in st.session_state.chat_history: chat_content += f"<div style='background-color: #222222; color: white; padding: 10px;'>**You:** {user_msg}</div>" chat_content += f"<div style='background-color: #333333; color: white; padding: 10px;'>**Zi GPT:** {bot_resp}</div>" st.markdown(chat_content, unsafe_allow_html=True) if __name__ == '__main__': main() Give me some recommedations on what should I do or change. ### Suggestion: _No response_

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I_kwDOIPDwls57Dm1Y

PGVector method for HNSW

open

NONE

### Feature request Provide a method to create the HNSW for the PGVector vectorstore ### Motivation There is a similar method implemented for PGEmbedding but the embedding extension will be deprecated ### Your contribution https://github.com/pgvector/pgvector?tab=readme-ov-file#hnsw https://github.com/langchain-ai/langchain/blob/6e90b7a91bba16d84689d07d1016a941eddf4f64/libs/community/langchain_community/vectorstores/pgembedding.py#L184-L212

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PR_kwDOIPDwls5jKfKA

update chain docs

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I_kwDOIPDwls57Dd3W

Issue: No connection adaptors were found

open

NONE

### Issue you'd like to raise. Hi all, I am trying to create an app that can act upon a natural language prompt to interact with the Monday API and then successfully carry out the relevant action. The code I currently have is as follows: > MondayDocs = """ > > > The below is Monday API documentation showing how to create an item. > > The endpoint is: > > "https://api.monday.com/v2". > > ///Example to create a new item: > > > mutation_query = ''' > mutation { > create_item (board_id: id, item_name: "itemname") { > id > } > } > ''' > > > data = {'query': mutation_query} > > > response = requests.post(url=apiUrl, json=data, headers=headers) > > > Use the below authorisation: > > headers = {"Authorization": "X"} > > > """ > > > llm = ChatOpenAI(temperature=0, model= 'gpt-3.5-turbo-1106', openai_api_key="Y") > > Test = APIChain.from_llm_and_api_docs(llm, MondayDocs,limit_to_domains=None, verbose=True) > > Test.run("Create a new item named James.") > However, when I run this, I get the error message: > No connection adapters were found How can I fix this? ### Suggestion: _No response_

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PR_kwDOIPDwls5jKYok

fix: Ollama import statements

closed

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PR_kwDOIPDwls5jKVnI

langchain[patch]: bump community >=0.0.8,<0.1

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I_kwDOIPDwls57DJ_8

`langchain-core` cannot import name 'tracing_enabled'

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### System Info ``` bash bash-4.2# pip freeze | grep langchain langchain==0.0.353 langchain-community==0.0.8 langchain-core==0.1.5 bash-4.2# python --version Python 3.10.13 bash-4.2# uname -a Linux 5b9ca59024db 6.1.61-85.141.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Wed Nov 8 00:39:18 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux ``` ### Who can help? @hwchase17 I looks like `langchain-core==0.1.5` has a problem with `'tracing_enabled'` because the V1 was deleted but some references to it remained: ``` python File <ourfile>:5 2 import os 3 from typing import Any ----> 5 from langchain.chains import create_extraction_chain_pydantic 6 from langchain.chat_models import AzureChatOpenAI 7 from langchain.chains.base import Chain File /var/lang/lib/python3.10/site-packages/langchain/chains/__init__.py:20 1 """**Chains** are easily reusable components linked together. 2 3 Chains encode a sequence of calls to components like models, document retrievers, (...) 17 Chain --> <name>Chain # Examples: LLMChain, MapReduceChain, RouterChain 18 """ ---> 20 from langchain.chains.api.base import APIChain 21 from langchain.chains.api.openapi.chain import OpenAPIEndpointChain 22 from langchain.chains.combine_documents.base import AnalyzeDocumentChain File /var/lang/lib/python3.10/site-packages/langchain/chains/api/base.py:11 8 from langchain_core.prompts import BasePromptTemplate 9 from langchain_core.pydantic_v1 import Field, root_validator ---> 11 from langchain.callbacks.manager import ( 12 AsyncCallbackManagerForChainRun, 13 CallbackManagerForChainRun, 14 ) 15 from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT 16 from langchain.chains.base import Chain File /var/lang/lib/python3.10/site-packages/langchain/callbacks/__init__.py:45 40 from langchain_community.callbacks.whylabs_callback import WhyLabsCallbackHandler 41 from langchain_core.callbacks import ( 42 StdOutCallbackHandler, 43 StreamingStdOutCallbackHandler, 44 ) ---> 45 from langchain_core.tracers.context import ( 46 collect_runs, 47 tracing_enabled, 48 tracing_v2_enabled, 49 ) 50 from langchain_core.tracers.langchain import LangChainTracer 52 from langchain.callbacks.file import FileCallbackHandler ImportError: cannot import name 'tracing_enabled' from 'langchain_core.tracers.context' (/var/lang/lib/python3.10/site-packages/langchain_core/tracers/context.py) ``` Reverting to `langchain-core==0.1.4` fixed the issue for me ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction Install core version `0.1.5` and `from langchain.chains import create_extraction_chain_pydantic` ### Expected behavior No error

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PR_kwDOIPDwls5jKCIN

infra: update community test min reqs

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PR_kwDOIPDwls5jKA_w

community: changing the .dict() to .model_dump() since it is deprecated

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- **Description:** The method `.dict()` was replaced by `.model_dump` since its deprecated in Pydantic V2 and will be removed in Pydantic V.3, - **Issue:** None, - **Dependencies:** None, - **Twitter handle:** @hhvedoveli <!-- 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_kwDOIPDwls5jJ9lU

community[patch]: bump core version >=0.1.5,<0.2

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I_kwDOIPDwls57C4MC

HELP!: Example of using HuggingFaceTextGenInference, llmchain, and fastapi

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### Issue you'd like to raise. I've scoured the internet trying to find an example that would use a custom model (Mistral) with ```HuggingFaceTextGenInference``` which uses ```LLMChain``` to return a **streaming** response via ```fastapi```. Does anyone have a working example? ### Suggestion: _No response_

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PR_kwDOIPDwls5jJ2RF

infra: fix min deps test

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