Fix typos (#11663)

langchain-ai · Oct 12, 2023 · bb2ed46 · bb2ed46
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diff --git a/cookbook/elasticsearch_db_qa.ipynb b/cookbook/elasticsearch_db_qa.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Elasticsearch\n",
     "\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/qa_structured/integrations/elasticsearch.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/qa_structured/integrations/elasticsearch.ipynb)\n",
     "\n",
     "We can use LLMs to interact with Elasticsearch analytics databases in natural language.\n",
     "\n",

diff --git a/cookbook/sales_agent_with_context.ipynb b/cookbook/sales_agent_with_context.ipynb
@@ -66,7 +66,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# install aditional dependencies\n",
+    "# install additional dependencies\n",
     "# ! pip install chromadb openai tiktoken"
    ]
   },

diff --git a/cookbook/smart_llm.ipynb b/cookbook/smart_llm.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "Note that SmartLLMChains\n",
     "- use more LLM passes (ie n+2 instead of just 1)\n",
-    "- only work then the underlying LLM has the capability for reflection, whicher smaller models often don't\n",
+    "- only work then the underlying LLM has the capability for reflection, which smaller models often don't\n",
     "- only work with underlying models that return exactly 1 output, not multiple\n",
     "\n",
     "This notebook demonstrates how to use a SmartLLMChain."
@@ -241,7 +241,7 @@
     "    ideation_llm=ChatOpenAI(temperature=0.9, model_name=\"gpt-4\"),\n",
     "    llm=ChatOpenAI(\n",
     "        temperature=0, model_name=\"gpt-4\"\n",
-    "    ),  # will be used for critqiue and resolution as no specific llms are given\n",
+    "    ),  # will be used for critique and resolution as no specific llms are given\n",
     "    prompt=prompt,\n",
     "    n_ideas=3,\n",
     "    verbose=True,\n",

diff --git a/docs/README.md b/docs/README.md
@@ -42,7 +42,7 @@ If you are using GitHub pages for hosting, this command is a convenient way to b
 
 ### Continuous Integration
 
-Some common defaults for linting/formatting have been set for you. If you integrate your project with an open source Continuous Integration system (e.g. Travis CI, CircleCI), you may check for issues using the following command.
+Some common defaults for linting/formatting have been set for you. If you integrate your project with an open-source Continuous Integration system (e.g. Travis CI, CircleCI), you may check for issues using the following command.
 
 ```
 $ yarn ci

diff --git a/docs/docs/additional_resources/youtube.mdx b/docs/docs/additional_resources/youtube.mdx
@@ -91,7 +91,7 @@
 - [Chat with a `CSV` | `LangChain Agents` Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
 - [Create Your Own ChatGPT with `PDF` Data in 5 Minutes (LangChain Tutorial)](https://youtu.be/au2WVVGUvc8) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
 - [Build a Custom Chatbot with OpenAI: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU) by [Fabrikod](https://www.youtube.com/@fabrikod)
-- [`Flowise` is an open source no-code UI visual tool to build 🦜🔗LangChain applications](https://youtu.be/CovAPtQPU0k) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
+- [`Flowise` is an open-source no-code UI visual tool to build 🦜🔗LangChain applications](https://youtu.be/CovAPtQPU0k) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
 - [LangChain & GPT 4 For Data Analysis: The `Pandas` Dataframe Agent](https://youtu.be/rFQ5Kmkd4jc) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
 - [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Toolfinder AI](https://www.youtube.com/@toolfinderai)
 - [How to build with Langchain 10x easier | ⛓️ LangFlow & `Flowise`](https://youtu.be/Ya1oGL7ZTvU) by [AI Jason](https://www.youtube.com/@AIJasonZ)

diff --git a/docs/docs/expression_language/how_to/map.ipynb b/docs/docs/expression_language/how_to/map.ipynb
@@ -101,7 +101,7 @@
    "source": [
     "Here the input to prompt is expected to be a map with keys \"context\" and \"question\". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the \"question\" key.\n",
     "\n",
-    "Note that when composing a RunnableMap when another Runnable we don't even need to wrap our dictuionary in the RunnableMap class — the type conversion is handled for us."
+    "Note that when composing a RunnableMap when another Runnable we don't even need to wrap our dictionary in the RunnableMap class — the type conversion is handled for us."
    ]
   },
   {

diff --git a/docs/docs/guides/evaluation/comparison/custom.ipynb b/docs/docs/guides/evaluation/comparison/custom.ipynb
@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Custom Pairwise Evaluator\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/custom.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/custom.ipynb)\n",
                 "\n",
                 "You can make your own pairwise string evaluators by inheriting from `PairwiseStringEvaluator` class and overwriting the `_evaluate_string_pairs` method (and the `_aevaluate_string_pairs` method if you want to use the evaluator asynchronously).\n",
                 "\n",
@@ -28,7 +28,7 @@
                 "from langchain.evaluation import PairwiseStringEvaluator\n",
                 "\n",
                 "\n",
-                "class LengthComparisonPairwiseEvalutor(PairwiseStringEvaluator):\n",
+                "class LengthComparisonPairwiseEvaluator(PairwiseStringEvaluator):\n",
                 "    \"\"\"\n",
                 "    Custom evaluator to compare two strings.\n",
                 "    \"\"\"\n",
@@ -66,7 +66,7 @@
                 }
             ],
             "source": [
-                "evaluator = LengthComparisonPairwiseEvalutor()\n",
+                "evaluator = LengthComparisonPairwiseEvaluator()\n",
                 "\n",
                 "evaluator.evaluate_string_pairs(\n",
                 "    prediction=\"The quick brown fox jumped over the lazy dog.\",\n",

diff --git a/docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb b/docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb
@@ -8,7 +8,7 @@
             },
             "source": [
                 "# Pairwise Embedding Distance \n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb)\n",
                 "\n",
                 "One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
                 "\n",
@@ -86,7 +86,7 @@
             "source": [
                 "## Select the Distance Metric\n",
                 "\n",
-                "By default, the evalutor uses cosine distance. You can choose a different distance metric if you'd like. "
+                "By default, the evaluator uses cosine distance. You can choose a different distance metric if you'd like. "
             ]
         },
         {
@@ -230,4 +230,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 4
-}
+}
diff --git a/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb b/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb
@@ -6,13 +6,13 @@
             "metadata": {},
             "source": [
                 "# Pairwise String Comparison\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb)\n",
                 "\n",
                 "Often you will want to compare predictions of an LLM, Chain, or Agent for a given input. The `StringComparison` evaluators facilitate this so you can answer questions like:\n",
                 "\n",
                 "- Which LLM or prompt produces a preferred output for a given question?\n",
                 "- Which examples should I include for few-shot example selection?\n",
-                "- Which output is better to include for fintetuning?\n",
+                "- Which output is better to include for fine-tuning?\n",
                 "\n",
                 "The simplest and often most reliable automated way to choose a preferred prediction for a given input is to use the `pairwise_string` evaluator.\n",
                 "\n",
@@ -379,4 +379,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}
diff --git a/docs/docs/guides/evaluation/examples/comparisons.ipynb b/docs/docs/guides/evaluation/examples/comparisons.ipynb
@@ -5,7 +5,7 @@
             "metadata": {},
             "source": [
                 "# Comparing Chain Outputs\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/examples/comparisons.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/examples/comparisons.ipynb)\n",
                 "\n",
                 "Suppose you have two different prompts (or LLMs). How do you know which will generate \"better\" results?\n",
                 "\n",
@@ -16,7 +16,7 @@
                 "2. A dataset of inputs\n",
                 "3. 2 (or more) LLMs, Chains, or Agents to compare\n",
                 "\n",
-                "Then we will aggregate the restults to determine the preferred model.\n",
+                "Then we will aggregate the results to determine the preferred model.\n",
                 "\n",
                 "### Step 1. Create the Evaluator\n",
                 "\n",
@@ -445,4 +445,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 4
-}
+}
diff --git a/docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb b/docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb
@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Criteria Evaluation\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb)\n",
                 "\n",
                 "In scenarios where you wish to assess a model's output using a specific rubric or criteria set, the `criteria` evaluator proves to be a handy tool. It allows you to verify if an LLM or Chain's output complies with a defined set of criteria.\n",
                 "\n",
@@ -73,7 +73,7 @@
                 "- prediction (str) – The predicted response.\n",
                 "\n",
                 "The criteria evaluators return a dictionary with the following values:\n",
-                "- score: Binary integeer 0 to 1, where 1 would mean that the output is compliant with the criteria, and 0 otherwise\n",
+                "- score: Binary integer 0 to 1, where 1 would mean that the output is compliant with the criteria, and 0 otherwise\n",
                 "- value: A \"Y\" or \"N\" corresponding to the score\n",
                 "- reasoning: String \"chain of thought reasoning\" from the LLM generated prior to creating the score"
             ]

diff --git a/docs/docs/guides/evaluation/string/custom.ipynb b/docs/docs/guides/evaluation/string/custom.ipynb
@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Custom String Evaluator\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/custom.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/custom.ipynb)\n",
                 "\n",
                 "You can make your own custom string evaluators by inheriting from the `StringEvaluator` class and implementing the `_evaluate_strings` (and `_aevaluate_strings` for async support) methods.\n",
                 "\n",

diff --git a/docs/docs/guides/evaluation/string/embedding_distance.ipynb b/docs/docs/guides/evaluation/string/embedding_distance.ipynb
@@ -7,7 +7,7 @@
             },
             "source": [
                 "# Embedding Distance\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/embedding_distance.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/embedding_distance.ipynb)\n",
                 "\n",
                 "To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector vector distance metric the two embedded representations using the `embedding_distance` evaluator.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
                 "\n",
@@ -80,7 +80,7 @@
             "source": [
                 "## Select the Distance Metric\n",
                 "\n",
-                "By default, the evalutor uses cosine distance. You can choose a different distance metric if you'd like. "
+                "By default, the evaluator uses cosine distance. You can choose a different distance metric if you'd like. "
             ]
         },
         {
@@ -221,4 +221,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 4
-}
+}
diff --git a/docs/docs/guides/evaluation/string/exact_match.ipynb b/docs/docs/guides/evaluation/string/exact_match.ipynb
@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Exact Match\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/exact_match.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/exact_match.ipynb)\n",
                 "\n",
                 "Probably the simplest ways to evaluate an LLM or runnable's string output against a reference label is by a simple string equivalence.\n",
                 "\n",

diff --git a/docs/docs/guides/evaluation/string/regex_match.ipynb b/docs/docs/guides/evaluation/string/regex_match.ipynb
@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Regex Match\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/regex_match.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/regex_match.ipynb)\n",
                 "\n",
                 "To evaluate chain or runnable string predictions against a custom regex, you can use the `regex_match` evaluator."
             ]

diff --git a/docs/docs/guides/evaluation/string/string_distance.ipynb b/docs/docs/guides/evaluation/string/string_distance.ipynb
@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# String Distance\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
                 "\n",
                 "One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
                 "\n",

diff --git a/docs/docs/guides/evaluation/trajectory/custom.ipynb b/docs/docs/guides/evaluation/trajectory/custom.ipynb
@@ -6,7 +6,7 @@
             "metadata": {},
             "source": [
                 "# Custom Trajectory Evaluator\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/custom.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/custom.ipynb)\n",
                 "\n",
                 "You can make your own custom trajectory evaluators by inheriting from the [AgentTrajectoryEvaluator](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.AgentTrajectoryEvaluator.html#langchain.evaluation.schema.AgentTrajectoryEvaluator) class and overwriting the `_evaluate_agent_trajectory` (and `_aevaluate_agent_action`) method.\n",
                 "\n",

diff --git a/docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb b/docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb
@@ -8,7 +8,7 @@
             },
             "source": [
                 "# Agent Trajectory\n",
-                "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb)\n",
+                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/trajectory/trajectory_eval.ipynb)\n",
                 "\n",
                 "Agents can be difficult to holistically evaluate due to the breadth of actions and generation they can make. We recommend using multiple evaluation techniques appropriate to your use case. One way to evaluate an agent is to look at the whole trajectory of actions taken along with their responses.\n",
                 "\n",