[Docs] : Fix typos in docs

docs/docs/gpt-researcher/context/local-docs.md

@@ -11,12 +11,12 @@ export DOC_PATH="./my-docs"
 ```
 
 Step 2: 
- - If you're running the frontend app on localhost:8000, simply select "My Documents" from the the "Report Source" Dropdown Options.
+ - If you're running the frontend app on localhost:8000, simply select "My Documents" from the "Report Source" Dropdown Options.
  - If you're running GPT Researcher with the [PIP package](https://docs.tavily.com/docs/gpt-researcher/gptr/pip-package), pass the `report_source` argument as "local" when you instantiate the `GPTResearcher` class [code sample here](https://docs.gptr.dev/docs/gpt-researcher/context/tailored-research).
 
 ## Local Docs + Web (Hybrid)
 
 ![GPT Researcher hybrid research](./gptr-hybrid.png)
 
 Check out the blog post on [Hybrid Research](https://docs.gptr.dev/blog/gptr-hybrid) to learn more about how to combine local documents with web research.
-```
+```

docs/docs/gpt-researcher/context/tailored-research.md

@@ -89,7 +89,7 @@ You can combine the above methods to conduct hybrid research. For example, you c
 Simply provide the sources and set the `report_source` argument as `"hybrid"` and watch the magic happen.
 
 Please note! You should set the proper retrievers for the web sources and doc path for local documents for this to work.
-To lean more about retrievers check out the [Retrievers](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers) documentation.
+To learn more about retrievers check out the [Retrievers](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers) documentation.
 
 
 ### Research on LangChain Documents 🦜️🔗

docs/docs/gpt-researcher/context/vector-stores.md

@@ -68,7 +68,7 @@ query = """
     Summarize the essay into 3 or 4 succinct sections.
     Make sure to include key points regarding wealth creation.
 
-    Include some recommendations for entrepeneurs in the conclusion.
+    Include some recommendations for entrepreneurs in the conclusion.
 """
 
 
@@ -145,11 +145,11 @@ researcher = GPTResearcher(
     vector_store=vector_store, 
 )
 
-# Conduct research, the context will be chunked and store in the vector_store
+# Conduct research, the context will be chunked and stored in the vector_store
 await researcher.conduct_research()
 
 # Query the 5 most relevant context in our vector store
 related_contexts = await vector_store.asimilarity_search("GPT-4", k = 5) 
 print(related_contexts)
 print(len(related_contexts)) #Should be 5 
-```
+```

docs/docs/gpt-researcher/llms/llms.md

@@ -7,7 +7,7 @@ Current supported LLMs are `openai`, `anthropic`, `azure_openai`, `cohere`, `goo
 Using any model will require updating the `SMART_LLM` and `FAST_LLM` env vars. You might also need to include the LLM provider API Key.
 To learn more about support customization options see [here](/gpt-researcher/config).
 
-**Please note**: GPT Researcher is optimized and heavily tested on GPT models. Some other models might run intro context limit errors, and unexpected responses.
+**Please note**: GPT Researcher is optimized and heavily tested on GPT models. Some other models might run into context limit errors, and unexpected responses.
 Please provide any feedback in our [Discord community](https://discord.gg/DUmbTebB) channel, so we can better improve the experience and performance.
 
 Below you can find examples for how to configure the various supported LLMs.